{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Forest applied to LendingClub data set \n",
    "\n",
    "For this exercise, we will be exploring publicly available data from [LendingClub.com](www.lendingclub.com). Lending Club connects people who need money (borrowers) with people who have money (investors). We try to create a model to predict the risk of lending money to someone given a wide range of credit related data. We will use lending data from 2007-2010 and be trying to classify and predict **whether or not the borrower paid back their loan in full.**\n",
    "\n",
    "Here are what the columns in the data set represent:\n",
    "\n",
    "* **credit.policy**: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.\n",
    "* **purpose**: The purpose of the loan (takes values \"credit_card\", \"debt_consolidation\", \"educational\", \"major_purchase\", \"small_business\", and \"all_other\").\n",
    "* **int.rate**: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.\n",
    "* **installment**: The monthly installments owed by the borrower if the loan is funded.\n",
    "* **log.annual.inc**: The natural log of the self-reported annual income of the borrower.\n",
    "* **dti**: The debt-to-income ratio of the borrower (amount of debt divided by annual income).\n",
    "* **fico**: The FICO credit score of the borrower.\n",
    "* **days.with.cr.line**: The number of days the borrower has had a credit line.\n",
    "* **revol.bal**: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).\n",
    "* **revol.util**: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).\n",
    "* **inq.last.6mths**: The borrower's number of inquiries by creditors in the last 6 months.\n",
    "* **delinq.2yrs**: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.\n",
    "* **pub.rec**: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).\n",
    "* **not.fully.paid**: The quantity of interest for classification - whether the borrower paid back the money in full or not"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries and data set\n",
    "\n",
    "**Import the usual libraries for pandas and plotting**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the Data\n",
    "\n",
    "** Use pandas to read loan_data.csv**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('loan_data.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check out the info(), head(), and describe() methods on loans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9578 entries, 0 to 9577\n",
      "Data columns (total 14 columns):\n",
      "credit.policy        9578 non-null int64\n",
      "purpose              9578 non-null object\n",
      "int.rate             9578 non-null float64\n",
      "installment          9578 non-null float64\n",
      "log.annual.inc       9578 non-null float64\n",
      "dti                  9578 non-null float64\n",
      "fico                 9578 non-null int64\n",
      "days.with.cr.line    9578 non-null float64\n",
      "revol.bal            9578 non-null int64\n",
      "revol.util           9578 non-null float64\n",
      "inq.last.6mths       9578 non-null int64\n",
      "delinq.2yrs          9578 non-null int64\n",
      "pub.rec              9578 non-null int64\n",
      "not.fully.paid       9578 non-null int64\n",
      "dtypes: float64(6), int64(7), object(1)\n",
      "memory usage: 1.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
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       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "      <td>9578.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.804970</td>\n",
       "      <td>0.122640</td>\n",
       "      <td>319.089413</td>\n",
       "      <td>10.932117</td>\n",
       "      <td>12.606679</td>\n",
       "      <td>710.846314</td>\n",
       "      <td>4560.767197</td>\n",
       "      <td>1.691396e+04</td>\n",
       "      <td>46.799236</td>\n",
       "      <td>1.577469</td>\n",
       "      <td>0.163708</td>\n",
       "      <td>0.062122</td>\n",
       "      <td>0.160054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.396245</td>\n",
       "      <td>0.026847</td>\n",
       "      <td>207.071301</td>\n",
       "      <td>0.614813</td>\n",
       "      <td>6.883970</td>\n",
       "      <td>37.970537</td>\n",
       "      <td>2496.930377</td>\n",
       "      <td>3.375619e+04</td>\n",
       "      <td>29.014417</td>\n",
       "      <td>2.200245</td>\n",
       "      <td>0.546215</td>\n",
       "      <td>0.262126</td>\n",
       "      <td>0.366676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.060000</td>\n",
       "      <td>15.670000</td>\n",
       "      <td>7.547502</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>612.000000</td>\n",
       "      <td>178.958333</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.103900</td>\n",
       "      <td>163.770000</td>\n",
       "      <td>10.558414</td>\n",
       "      <td>7.212500</td>\n",
       "      <td>682.000000</td>\n",
       "      <td>2820.000000</td>\n",
       "      <td>3.187000e+03</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.122100</td>\n",
       "      <td>268.950000</td>\n",
       "      <td>10.928884</td>\n",
       "      <td>12.665000</td>\n",
       "      <td>707.000000</td>\n",
       "      <td>4139.958333</td>\n",
       "      <td>8.596000e+03</td>\n",
       "      <td>46.300000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140700</td>\n",
       "      <td>432.762500</td>\n",
       "      <td>11.291293</td>\n",
       "      <td>17.950000</td>\n",
       "      <td>737.000000</td>\n",
       "      <td>5730.000000</td>\n",
       "      <td>1.824950e+04</td>\n",
       "      <td>70.900000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.216400</td>\n",
       "      <td>940.140000</td>\n",
       "      <td>14.528354</td>\n",
       "      <td>29.960000</td>\n",
       "      <td>827.000000</td>\n",
       "      <td>17639.958330</td>\n",
       "      <td>1.207359e+06</td>\n",
       "      <td>119.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       credit.policy     int.rate  installment  log.annual.inc          dti  \\\n",
       "count    9578.000000  9578.000000  9578.000000     9578.000000  9578.000000   \n",
       "mean        0.804970     0.122640   319.089413       10.932117    12.606679   \n",
       "std         0.396245     0.026847   207.071301        0.614813     6.883970   \n",
       "min         0.000000     0.060000    15.670000        7.547502     0.000000   \n",
       "25%         1.000000     0.103900   163.770000       10.558414     7.212500   \n",
       "50%         1.000000     0.122100   268.950000       10.928884    12.665000   \n",
       "75%         1.000000     0.140700   432.762500       11.291293    17.950000   \n",
       "max         1.000000     0.216400   940.140000       14.528354    29.960000   \n",
       "\n",
       "              fico  days.with.cr.line     revol.bal   revol.util  \\\n",
       "count  9578.000000        9578.000000  9.578000e+03  9578.000000   \n",
       "mean    710.846314        4560.767197  1.691396e+04    46.799236   \n",
       "std      37.970537        2496.930377  3.375619e+04    29.014417   \n",
       "min     612.000000         178.958333  0.000000e+00     0.000000   \n",
       "25%     682.000000        2820.000000  3.187000e+03    22.600000   \n",
       "50%     707.000000        4139.958333  8.596000e+03    46.300000   \n",
       "75%     737.000000        5730.000000  1.824950e+04    70.900000   \n",
       "max     827.000000       17639.958330  1.207359e+06   119.000000   \n",
       "\n",
       "       inq.last.6mths  delinq.2yrs      pub.rec  not.fully.paid  \n",
       "count     9578.000000  9578.000000  9578.000000     9578.000000  \n",
       "mean         1.577469     0.163708     0.062122        0.160054  \n",
       "std          2.200245     0.546215     0.262126        0.366676  \n",
       "min          0.000000     0.000000     0.000000        0.000000  \n",
       "25%          0.000000     0.000000     0.000000        0.000000  \n",
       "50%          1.000000     0.000000     0.000000        0.000000  \n",
       "75%          2.000000     0.000000     0.000000        0.000000  \n",
       "max         33.000000    13.000000     5.000000        1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>purpose</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>credit_card</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy             purpose  int.rate  installment  log.annual.inc  \\\n",
       "0              1  debt_consolidation    0.1189       829.10       11.350407   \n",
       "1              1         credit_card    0.1071       228.22       11.082143   \n",
       "2              1  debt_consolidation    0.1357       366.86       10.373491   \n",
       "3              1  debt_consolidation    0.1008       162.34       11.350407   \n",
       "4              1         credit_card    0.1426       102.92       11.299732   \n",
       "\n",
       "     dti  fico  days.with.cr.line  revol.bal  revol.util  inq.last.6mths  \\\n",
       "0  19.48   737        5639.958333      28854        52.1               0   \n",
       "1  14.29   707        2760.000000      33623        76.7               0   \n",
       "2  11.63   682        4710.000000       3511        25.6               1   \n",
       "3   8.10   712        2699.958333      33667        73.2               1   \n",
       "4  14.97   667        4066.000000       4740        39.5               0   \n",
       "\n",
       "   delinq.2yrs  pub.rec  not.fully.paid  \n",
       "0            0        0               0  \n",
       "1            0        0               0  \n",
       "2            0        0               0  \n",
       "3            0        0               0  \n",
       "4            1        0               0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Follwoing is a breakup of credit approval status. 1 means approved credit, 0 means not approved.\n",
      "1    7710\n",
      "0    1868\n",
      "Name: credit.policy, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Follwoing is a breakup of credit approval status. 1 means approved credit, 0 means not approved.\")\n",
    "print(df['credit.policy'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histogram of FICO scores by credit approval status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a5fb828198>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bb/yb6dPv4ZhjTgAgL+/nzmW5uTlstVU3xo27drXp2rYt5OuvQ1N7Yie0nJzc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\nwTsiIqJhErwjIiIaJsE7IiKiYf4AKHvxs4Nel5oAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a5fcbbbf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df['credit.policy']==1]['fico'].plot.hist(bins=30,alpha=0.5,color='blue', label='Credit.Policy=1')\n",
    "df[df['credit.policy']==0]['fico'].plot.hist(bins=30,alpha=0.5, color='red', label='Credit.Policy=0')\n",
    "plt.legend(fontsize=15)\n",
    "plt.title (\"Histogram of FICO score by approved or disapproved credit policies\", fontsize=16)\n",
    "plt.xlabel(\"FICO score\", fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Presence or absence of statistical difference of various factors between credit approval status"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a5fb0a7a58>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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y+2HixNMzjqRzaGgYyOTJF2QdxhZL2337T2zay+0C4Ax3V6OKSAU0NS1h8ZK3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a5fcf37438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['credit.policy'],y=df['int.rate'])\n",
    "plt.title(\"Interest rate varies between risky and non-risky borrowers\", fontsize=15)\n",
    "plt.xlabel(\"Credit policy\",fontsize=15)\n",
    "plt.ylabel(\"Interest rate\",fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a5fd01a3c8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XL7IuAX5v4Sb2PxOS806Ea4vZ73e7mRUudF8E1AO3e+gRfAdwnYWemC8RduKjgT3cfYWZ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\nIiIl+n8rO3zDHAAfWgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a5fcf33198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['credit.policy'],y=df['log.annual.inc'])\n",
    "plt.title(\"Income level does not make a big difference in credit approval odds\", fontsize=15)\n",
    "plt.xlabel(\"Credit policy\",fontsize=15)\n",
    "plt.ylabel(\"Log. annual income\",fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a5fd301048>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ssbp27dpUAuzaVTcfEJHqpKGUpE122WVXHn304aZpEZFqpKGUpE1mznw177SI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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a5fd0faf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['credit.policy'],y=df['days.with.cr.line'])\n",
    "plt.title(\"Credit-approved users have a slightly higher days with credit line\", fontsize=15)\n",
    "plt.xlabel(\"Credit policy\",fontsize=15)\n",
    "plt.ylabel(\"Days with credit line\",fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sns.boxplot(x=df['credit.policy'],y=df['dti'])\n",
    "plt.title(\"Debt-to-income level does not make a big difference in credit approval odds\", fontsize=15)\n",
    "plt.xlabel(\"Credit policy\",fontsize=15)\n",
    "plt.ylabel(\"Debt-to-income ratio\",fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Countplot of loans by purpose, with the color hue defined by not.fully.paid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a5fd630e10>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DM0mSpAYxOJMkSWoQgzNJkqQGGd2NmUbEIsDpwKrAYsCRwN+Bi4E/19FOzswf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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a5fb2869b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "sns.countplot(x='purpose',hue='not.fully.paid',data=df, palette='Set1')\n",
    "plt.title(\"Bar chart of loan purpose colored by not fully paid status\", fontsize=17)\n",
    "plt.xlabel(\"Purpose\", fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trend between FICO score and interest rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x2a5ff68be10>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6x/KxuqT6xfm9NigOdu8ZAACKgSuZzO3GrsWgu7u//E4KOefkdBC7D5A/uvEh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\nxpmMLlRCNoaQfRPs9FtrK+fWRA9OrOthiG4o7S3xwT/Fj07BjpxvMl0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TjDhk31w6bbIB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08e28bGffqDj4XnVT/9wcdDMh4gf0ZW1knSaTJlnnYYC2tUKBSjG5WQKUyx2xw2XWJ6DN7JhA37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XS3\nY0eb4TJOYWQyZ03BUA9hVDBoK2QulyvG5XL92uVyHXO5XB+4XK4Zit9xuFyuIy6XazbncblcM1wu\n14cul+uwy+X6D5fLJSt7ghBldIuSNpU3wP1mMfktfn+Mb4MdvYWOHeMTQ/o2WMc39YFs9t+k9BnL\nZrE+To92an/dY9T16aZM1+0PCEgLXt7Ukxa8vCmktOC65zSildzJal/y5BTSp5stUfc+jfZK9VCj\nux072gyXcQqCED6DObH5OgC72+1eAOAfAfw0UHS5XLcBOARgegienwF43u12L0LPd5/LBnHcgiAE\noFuUtL25HS/PfgGv3voS3vv7d/HqrS/h5dkvoL2Zj6Pp8HXg3O9K4K3sid/xVrbg3O9K0OEzycKn\n4Rs704mYePVWuJh4q3LlCADGTE9jx8Lpk+7iU59Tum5mR3taAuujdOrFOhS9u1t9b3QR7UDPtbAS\n18LKXItItsnpnlPduB5dn262xIz8THaLpGp1LJJxSryTIAiCHoO5ZXEhgHcBwO12/7V3AhZIPIBv\nAHg1BM+XABhfdb4D4KsA3qI6HjvWgViTNMGCAAAZGclDPYSbnne/8+4NWwGNoqQJCXH42s+/Rvp+\nnPtj+GpvjEPx1frw2h0v4/vV379pfLbEWLQp4nNsibHk/WHn54awdwBphPdiIZ8QpPOqFxmz1S/K\nablpqC2uVbZTY9Xtr/Z8cD+BUMdYe74WnS3qE9TZ0sGem3nr5uGTrZ8o26njqz1fy26T4/oDgH+4\n9g/46YSfKtuTMpJI37J/fxgJCXFw73Gj4UoDUienwrXMha/+5KvKJA3G+MP1GXz38nfxi+xfwFvz\nRRIaR7oD3774bcQl0dkbv1/1ffxk/E9uKIJssVrwvcrvwZFOJ3zRHaeuT1Ajf6MEhyMOVuvQfXbk\nHowOgzkhSwEQuEep0+Vyxbrd7g4AcLvdRwDA5XKZegBY3G638dekCQCbj7mujk9JLQhAz0Omulod\nJyH04Pf6cWZ3iVIr3l2Cgu/eofzWu6m84YYXx0C8NV5cOFmu/FZ/KHxtterkFG21bUpfRkYyaqrU\nEwCDmqpGdKaoVwOafHyyjCZfm/K+bPV4UeeuU3rq3HX4/GyVMv6oeyy/KtE91qbsr6mRf47WN3rR\nWR38b1df5idylZdrlefG7/XDvU9dguDcvlJcvVyrLl0QCzYtuC8W/OfcCmy6/hwu/uU8in9zCrlP\nz0X2V6ajFd1oNXk+fOmf7kHus/PhKa6BMzcdCU4HPHXBmRn7P2u+9E/3YNbaPFw9VoGJCyYhOStV\n6evPuuJnUbbXjaJtnyFv/TzMeMiFhtY2oJW/p/7u2neDfC3dnWgJ4fgKvnvHDXFLoYxT1zfc8Hv9\nWjFdrR7vDfcMhfyNGh4M9oTF6+V3lQw2cg8OHNy9MpgTskYAgT3HGJOxcD0ulytwn0sygPoBGqMg\nCAy6Qfq6SQiGi+/6Z+rsdIE6tcVON3mFp7jmhlWOQLo7u+EprkHWoluCNN309Q0X+Zi/hosNyklu\nJMlAVJMqAGi60kjeazaHDVPun4ozr5wO0m65f6rpi3KHrwO7l+7qq5v1+YHLcOakY/m+JxFrp/9E\ndnV04ejmg7jwznk0VzQiaVIKpvXWlOJWgvr3Z9TpMuuvubIZOwpe7Pv56pEK/Bl7seb0BiSNp1fy\nfPU+bM/f2pel8eqRCrwX/y7WFm6EfYx6G2QgRtxSuOj6hgPRvvaCIIx8BnMN9AiApQDgcrnuAlAY\ngeeky+W6t/e/HwQweAVNBEHoQzdIXzcJwUj3AfrJK5y56bBY1ekDLVYLWYzYLJECpesWP44kGQgH\np5ftUZcKoNoD2b10l7Ju1u6lu1hfXyKRK409iUR6t/KaJRLR7S9wMhZKu0HgZMygs60T2/O3sj6B\nJtrXXhCEkc9gTsjeAuBzuVxHAfwbgO+6XK5VLpdrQzie3vZ/APAvLpfrGIA4AL8bxHELgtBLtJMQ\nDCdf3Bh13E7cmDjSB+gnr0hwOjBm1lilNmbWWHLrE7XCaab7atVFqM30UJKvqKg3iVmj9KbyBrQR\nRZPb6trY7J6tHi88JeoYO09JDVo96m2buolEdPu79jG/kkvpdaUetn6ZWY06IZhoX3tBEEYHg7ZG\n7na7uwA826/5rOL37jXxwO12nwMgFRAFYQgw0sZffOc8mq82IWliMrJ7t+dwrD6xHjtv23ZDog17\nmh2rT6wfEb6kCSmorQ9+wUqawGcnBIC1hRuDVi6s8VasLdzI+rxVRJwc0Q7o1yHT9enWvtL16W47\nBfS3gepu5dXtz/1GsdITqKu2uepew0BCjXcaSHT71I3pCodoX3tBEEYHsmlZEAQW3aKkcUlxeObs\nJjSVN9yQvGAk+Fo9XtSeVX/bXXu259tu7kWyubJZuY2subKZjOvpSUDiU2pttT40latjurIW8Wn2\nKV3XN2PZLJz85cekj6p9lbX4FtaXtVj9shrJ9lFnbjoQYwG6gl+ULTH8NlAukQi3DdRitShfzLlt\np9OXzULxDnrX/3TqnGpeQ2Bo4p10+9SN6dLBkZmIpInJPTXv+pE4CNdeEITRgeSgFQQhJHSLkiZn\npcL1eG5Ik6Ph4vMU1wBUrozuXp3ht1/eEVY7ENpKkIq4pHjWR+m6Pqq2lZlugTo+zkw3u16cnuB0\nKCdjANDd1U1Oqm0OG+yp6uOPT40nPyMJTgecOeoXb2cOvRqUYnKMlK57DYGhiXeKdjyfDjaHDR3E\ntsSOlvYBv/aCIIwOZEImCIIQJlY7X+eQ0927TbafEXpnpzoWyEx371GXLTDTa8/z8UWUfuXwZdZH\n6Ynj+aQelG4WB8XpumP1e/3w1qlj6FrrWtli1F9/+4mguEV7mh1ff/sJ0hOXQtcZ43RvDZ9qntKH\nIt4p2vF8urR6vPDVq1eqffU+9tws3/ck0vMy+pLzWKwWpOdlYPm+Jwd0jIIgDD9kQiYIghAmoaSu\npygMKLIdjn7xD+oaXWZ6sSIdfCh66Zv8RI7SS3byCXUpvaWSnzxQeihxUuGOxUz3VrXAW9Gs1Foq\nmuGtoo/l+I+OKAuYH//REdLT3sjXIaL0S3+6wPooPZR4p4FGt89QYroGEk9xDdBFiF386nisPRYr\nDjyFdUUb8ejvH8O6oo1YceApSXkvCILEkAmCIAA9KymX9l/E1AeyTRMdTH0gG8c2H2J1itx1Bbj+\n6X5WV+FaNQeX918ifa5Vc5TtM1fMxqmff0L6Zq6YrWzPWZ2PsrfoRBs5q/MH1JeazW/Lo/RIroXu\nWONS4th4IGrFymw1584fLFRuedPtTzeebyDinfoX2jZDt0/deL5AqgurULbnHGYsm2W65XYgzk1H\nqx8tlc2m93x/wnlGDYQPQNgxuQbRSLAiRIczO/gv9QaaOWvUfwNHOjIhEwRhVNO/cO6xzYf6Mh5S\nCTbGznQiJt6KLkVK8RgmdT0ATLqTT7RA6Rm5JrFZhJ4+M4P1UfrkRVNYH6Xr+nQLWJu9YHK67ljb\nG9vZ1Zz2xnZlTJBu8Wvd/nTj+RKcDiRPT0HjueCSAcnTU9h4p/pL9Xjtjpf7fja+RFh1/BmMmUoX\nik5wOuCYmoiW88Erj46piWw83+TFt6BkZ1GQlrX4FnYy4K3x4pW8X/eteJ385cdADLCu6Fk40tX9\nJTgdSJvthOdM8EpY2mwne27am9vJjK5xSfS2VJ1nVCS+SMYazQQrgjCSkE+HIAijGt3CuSnZ6vT2\nVLuBbgFkXV8kWQijSSSFoaNNtK/hUJwb1WSMazcInIyF0h6IajLGtRuUvBY8GePaDQInY3109bYz\nTLgzK6x2g/4THKBnu+rO27axPt1nVCRFwXXHGs0EK4IwkpAJmSAIoxbdwrmtHi8aSuuVWkNpPRvY\nr1uoufzo56yP0ot28XFSlH7858dYH6XvXb+H9VH6X557l/VR+vv/SG//NNMPPv8e66X006/wcYCU\nrusrfTuohGdI+icvHGd9lK5biPriX9TbMUPRy/a6WS+lVxdWsTFd1YVVA+rze/24tF8de3d5/wUy\niUhTeUPQBMfA11u2QoXuMyqSouC6Y412ghVBGEnIhEwQhFGLbkKISJIe6PZZ/JtTrI/Sz7zE+yi9\naNtnrI/SL/+Rfymn9It7TJKWELr7NZOslYx+ducZ1kvpuolZdH2nt37K+ihdt79QClGr0L1HAf37\nLZSC4gPp000iolu2Qvd5EUmyG92xRjvBiiCMJGRCJgjCqIVL+MDpRmC/CrPAft0+c5+ey/oofc63\neB+l562fx/oofcrDfAIHSs9eNpP1UbprVS7r4/TZq9WJUMz0/A3zWR+l6/oKNt7K+ihdtz/XEybn\nlNB171FA/36jEpOY6bo+I4mICi6JiO7WYd3nha6PG4uZrntuBEGQCZkgCKOYsTOdsMara4ZZmeQc\nkRR51e3TLFMdpS/4/kLWR+l3fGcB66P0h7YtY32U/uCvH2F9lL7kRw+wPk5fvOV+1kvpX9p0B+uj\ndF1f7kp1tkczXbe/CbfzL+SUrnuPAsCMh1ysl9Iz8jPpN5kYOnGJrs/msGHag8SXCg9OJ5OIJGel\nBtWfM7Cn2ckMhrrPC11fJGPVPTeCIMiETBCEUc7awo1BLy5GJjKOSIq86va56vgzYbUDMC3iS+m6\nPgC451f3htUO9MSfgEreFg8y/sTv9SPeqX55jE+3m8atfOW1h8Nq7xsrlQE8lR4rADy6d0VY7QaP\nHVgdVnsk/UVy7XXuUaDnnFnT1S/ssek29pyuK3o2+G2mN1sih67v7s2LUbBhPpInp8BitSB5cgoK\nNszH3ZsXs77VJ9Yri4KvPrGe9ek+L3R9kYxV99wIwmhH0t4LgjCqsY+xY+OVb4ddq8co8trq8cJT\nXANnLr8yFkhcUhzmrC1A2dul8FY2wzE+CTMencmmkzbGao233hCsb423simszYr4eoprkLXolgHz\nAcD0L8/CEXygbKfwVrUAVP3jNpAp4b1VLWirUycgaKtrI30GU++aCnuaPSi999S7pvJjbVJrlmYL\n2+f4/PFIz8tAzZlqoBuABUifk4Hx+ePJ/gAgfXY6CjbMR+FvT6O7vhOWMVbkryhA+my+7lVff0XV\nX/xbeXx/kVz7MVPHYNP158KuQ+atakGnRz3p6qj1s+fUke7ApsrnwqonBvR8DtNz+52b3AzTz2FM\nbAwWblmCO3+wMKxaW7H2WMx6LAelb59Da2ULEsYnYuajs0wLQ+s+o3R9QM+5eebsprDrkOmeG0EY\n7cgKmSAIAnq2+MzfdFvYhVMTnA5kLbol5MkY8EVqaG9lTzpvb2VzSKmhddJYmxWqpXRdHwDsKHgx\nrHagN3W7OiwPsGimhO/uNo1b0UnvHUmszO6lu3omAEZOmG6gpqgau5fuYsdp3DPd9T3Xv7u+M6R7\npq+/AMz6iyRG0mD8/AmY++xtGD9/gunvAj3nNDZRPRGyOeJCij9KmpiMyYunIGlickh96pybG8dl\nQ2r2mJAnHMY1bK3sSW7RWtkSVkp43WeUrg/o2b7oejw3rKLQQPjnRhBGOzIhEwRBiCK6qaF101jX\nX6hjx0PpZXtNstARum7KdF+t94tJSn+6e3XKx6Qvp3yAfnpvm8NGJkWY8kA2+RLa6vGipkS9+uQp\nqSG3Avq9fpzfV6bULuwrI++ZVo8XNcXVSq2muJrsL8HpwJhZY5XamFlj2S8fOnwd+O19r+KVvK14\n+5u/wyt5W/Hb+15Fh6+D9BhYuBuAQafPVo8XHo1roYukhBcEgUMmZIIgjCj8Xj8aLtaH/YKj66su\nrMKxLYfJukX90U0NrZvGWjeFuW6qdd3+dFNt6/oi9V77SK1R7UDvVkCNcgneqha0lKv3SDaX0/eM\np7iGnaxyWxO9VeoJCdVuYKw6GWUhuju7Q1p18la1kJ+9jtYONmW6Tp+RlK4wqCv14OQLJ9iaXgYD\nkRI+nP4C0X22ReoVBCF0JIZMEIQRQVdHF45uPogLQ4XXmwAAIABJREFU75xHc0UjkialYNqD03H3\n5sWIiaW/e9L1eWu8eCXv130vvCd/+XFfQgBHOr2C4MhMRNLEZDQrXrATme1uUx/IxrHNh8h/l1qx\ncT2Ri+IddHFoKoV5wcZbcfh7B0gflWo9a8ktbH9ZS9SxRylT+S1RlJ42m9+Gxem6fbZ6vPCcIVZX\nzvSsrqhWkVKz+f4oPS6Fj2midN3+msob0EasHLb1rhyqtrC1erxBWwANaoqqyfMC9HwuLGOs6K4N\nXgW2pMaQnwvdPiPZkuur992wffjY5kN9yTKoeE5HZiLiMuxorwo+r3Hp8eyWTJ3+AP1nW6ReQeCY\ns6ZgqIdwUyKfKkEQRgRGfEbzlUagC2i+0hhSfIauL3Ay1kdXbzuDzWFDB7US0NJObnczi/+gdN0U\n5vlr+LpQlN7pVW+rNNMbL6m3B5rpbfVtrI/TdfsMJemFii4/v/WO0tsbqWwnvK7bn+7Koe55AXo+\nF6rJGAB01XaSnwvdPs1iPjldJ5bT5rApJ2MA0FblY2OtdPoD9J9tkXoFQQgfmZAJgjDs0Y3P0PVV\nF1axW8G47YutHi989UTcUr2PjF0x26ZE6WZbKSldO11+QyvvI/Tm6mbWR+lH/g//gsjpnx+5zHop\n/eIB9T1jpn/20iesj9IvvmfSH6EX7TrF+ii9vdVkAkjo8WPiWR+nF79Or6py+tVPTCaPhK77udCN\n5dSNrdTtL5KYNYl3E4ToIxMyQRCGPbrxGbq+sj0mCS8YXTeuRzeGTHesuisPZb87y/dH6KVvlrA+\nSvd8qt6uFop+4b9LWS+lu3eZxMkRuvs1Ex+hn/kNP7Gi9JIdRayP0s+/5WZ9lF57lv/SgNN1YxbP\n/hd/jJSu+7mIdiynbn+RxKwNRLybIAjhIRMyQRCGPbppyHV9M5bR9bTMdN2U4lSMmJmuO1bdceZv\nmM/2R+lUTJqZnnEXX2+K02evnsN6KX3Ot+ayPkrPWZvH+ihd99zojjNvPb9dldInLuC3x3K67jHq\n+nQ/F7qfQypW00zX7S+S0gyReAVB0EMmZIIgDHtsDhumPaguPpv94HQyPkPXl5GfST89Y8AWpU1w\nOjDWlabUxrrSyNiVsTOdsMZblZo13krGkOmONcHpgDOHqFGWQxfBdi03efEk9NyV+ayP0h9/+29Z\nH6cv3nI/66X0Bd9fyPoofdE/L2F9lK4bz6c7zhkPuVgfpSdnpcKepk4yYU+zs7WsdK//vPVfYn2U\nrvu50P0c6sZy6van+2yL1CsIgh4yIRMEYURw9+bFKNgwH8mTU2CxWpA8OQUFG+bj7s2LB8W3rujZ\n4Cdob5ZFc5gKyAxrCzcGvZwZ2dY4dMe6fN+TSM/L6Fsps1gtSM/LwPJ9T7I+Xawp6sS/VLsBtQpm\ntnoGAJPuVWd9pNoNYrPUL6VUu8FX33g0rHaD5ftXhdVusPSt5WG1G6w8ui6sdoPVJ9YHTcrsaXas\nPrGe9QHAYwdWh9Vu8PV3V4bVbqD7uaBWs8xWwdac3hBWu4Hu51732RapVxCE8LF0d/NZmIYj1dVN\nI++ghAEnIyMZ1dXq2j7C8MXv9cNb1QJHZmJY3+SG6ut/31QXVqFszznMWDaLXRkzaPV48UreVmUN\nJIvVgnVFG00zwNWVenBp/0VMfSDbNPtiIOGONXDMnuIaOHPplbHAPt68/79I/fH3/lbZd1N5A169\n9SXS99Sn31KusPi9fuxatL0nG1w/kienYOXhteT11PWGew1Vz5rD//I+SrYXIWdtnunKWSCFOz7D\n6a2fomDjraYrZ4Ec+/GHOPPSKcz51lzTlbNAyva6UbTtM+Stn2e6chZIU3kDrh6rwMQFk9iVMRXF\nrxf2HaPZylkgn237pM9ntnIWSDifi0juN4NrH1fA/UYxXE/ksitn/e8b3c+97jMxUu9oISMjmf8m\nLUI++OmREfVOO5rT3nP3itQhEwRhRGFz2JCaPSZqvoz8zLAmN6EUpM1axK/OjJ3pDOuFzCDcsRok\nOB2mYzIIJVmCagyhpFp3PR78Yh9KAgLquup6B+IazvvWrRiXm2kad9Wf/DXzwpqIGSz4/sKwJmIG\nMx5yhTURM0jOSlVer1DIXZkf1kTMYM6qAmTfPz3sGKdwPheR3G8GE26fZLqFUYXu576j1Y+m8kbE\npcSFPanSfS4KghAeMiETBEGIIkayDGp1xaxg7c3OjGWzeopkM7oK3YQQRgIC1YpFKMkL7JkO+K4F\np/C3j0sgvc7c9J6tbqpsmTF8UeH25nbsvG0bfAGFl40tfXFJfAFoQU00ixg7MhORODEZLarC7hOS\nbqqEFx2+Duxeuguekp4vECxWC5w56Vi+70nE2uX1TxBuJiSGTBAEIYroJssYLpitNFB6clYqm7yA\n2vZmc9hgT1XXt4pPjTdNXuCrIuqpVXlJb4LTAfsYInnFGDt7DftPxgDAV+vDztu2kR6BJ5pFjG0O\nGxKYa38zbevbvXQXaoqq+7786e7sRk1RNXYv3TXEIxMEoT8yIRMEQYgAv9ePhov1YRVL7UuWEdOb\nLCMmvGQZTeUNcL9ZjKbyhrDG2urxovzw56ZFn/tTV+rByRdOmBanBnrOR9LEJKWWNCmZLdJtT09Q\navYMB+trZQptc9elrtTD1oTjiu7GEi/esYlxZJ9N5Q1Bk7G+sdb6wr6eQvSLGPu9fvga2pRaW0Pb\nTVM0udXjhadEXSvQU1IT9jMgWug8TwVhJCBr1oIgCBoMxDYpI6lSqMmVdLe76W5d8tX7sD1/Kzrb\nOgEAxzYf6svuRq0Qeata0FzZrNRaKpvJGBtvVQtarql93msmvqvq5DwtjA8IreiuKmbHW9WCZqpP\nJo5IN05OoBmImK6buT9dBiLOMZpEc9upINyMyF0uCIKgQSTbpIytRDDel7oR0lYi3e1uuluXAidj\nBp1tndiev5X0RLtIdyRFbCMpukttTYtNiCX7jKRwssHe9XvwwvifYe/6Paa/G8j+77yDFyb9G/Z/\n552wfGV73fjvb7yBsr3usHzu3cX43df+C+7dxWH5AOCTF47jlXlb8ckLx01/V/daBHLt4wp88L39\nuPYxP2E2+ou0aPKVw5fx541/xJXDl01/N5Dqwioc23IY1YVVpr+rW9i9P7orVuH6orntVBhazuw4\nHdL/RhuyQiYIghAmZtuk7vzBQvIlMZStRKoYpFC2u6nirFo9XtQUE/0V0/3VlXqCJmMGnW2dqCv1\nKFePbA4b4ibEA1eCfbYJdEyXzWFDSnaqOp14dirrs46LVfYXMy6Wjemxp6m3SIai+5uJLZREOwDT\n9O+cfuq3n+LI//ig7+fLb5/HC+N+hnt+dS/mrriV9BX9oQiHvvXnvp9LXytB6Wsl+PJLX0XeI3mk\nr7asFq/f/Urfz1ePVODP2IuVR9chbYa6sDkA1JytwW+/vKPv5/eefRfvPfsuVhxag/TZ/CSg/ONy\nvP3Qb/t+/mjzh/ho84d4dO8KZN2eRfq47awczZXN2FHwYt/PxTsKAfTUBUsar952a3PYMOmeLLhf\nD55oTrwni73fGssbsfPWL748KXurJyPp6k/XIyVLPckDAG+NF6/k/bpve+3JX37cVy/Nka6OVzQK\n0NcWB2+75QrQG+iuWOn4InmeCsJIQVbIBEEQwiSUbUsUoWwlUhHKdjeqP3QR/XXR/YWynY+i9nh1\nWO0GVw8pZlVMu0HDJ7VhtRtQx26mc9c3FF2HwMlYKO0GgZOxUNoNAidjobQbBE7GQmkPJHAyFko7\n0HuumThA7loETsZCaTdQTca4doPAyVgo7QaBk7E+unrbWfQK0AP6K1Y6vkiep4IwUpAJmSAIQphE\nsm1JdytR2my+/hClt1Sr47LM9GaToumU/taqN1kfpb//j/tZH6X/7huvsT5OP/wv77NeSv/w//Av\npZR+8Pn3WB+lm21PpHSz7YmUbrY9kdLNtidyutn2REqv+IifrFO62fZESjfbZkjpur7qwip2wklt\nX2z1eFHnVielqXN72KQeuolSdH0DsQ1UEIY7MiETBEEIE5vDhmkPTldq2Q9OZ7fX6Ka9b6tXZ3Yz\n08t+f5b1Ufr5t/gCz5R+7S/8CzKlu18zeZkn9OtHKlkfp9ed5rNGUvrlveqXTjP97M4zrI/SL//R\npD9CL32Tv/aUXrTtM9ZH6YUvnmR9nK7rLX6FjzWhdPcbJvcboZfsLGR9lK7rC6XQugrdlXhAf8VK\n1xfJ81QQRgoyIRMEQdDg7s2LUbBhPpInp8BitSB5cgoKNszH3ZsXm3r70t73rpRZrOZp75256X1p\n8vvDrazlPj2XHQul52+Yz/oofcJXJrM+SnetymV9lD7unvGsj9Odt2awXkrPXjaT9VH67NVzWB+l\nT3lY/bJqps98fDbro/S89fNYH6Xr3jOReHV9ridM7jdCz1mdz/ooXddHFVI30yNJ6jEUCXYieZ4K\nwkhAJmSCIAgaxMTGYOGWJVh5eC1WHX0aKw+vxcItS0JK0Rxrj8WKA09hXdFGPPr7x7CuaCNWHHiK\nTUGf4HSQL1Hcylr2V/iXeUr/0qY7WB+lf+O1x1kfpS/50QOsj9Ife2sV6+P0J959ivVS+oO/foT1\nUfriLfezPkp/aNsy1kfpD/z8QdZH6TMecrE+SnctN5nkMLru/abb54Tb+YyWlD550RTWR+m6voz8\nTPpNLYYutB5JAXrdFatIVroieZ4KwkhA7nRBEIQIsDlsSM0eo7WtJsHpQNaiW0wznhnorKwBwKrj\nz4TVbvDo3hVhtRvc9eOFYbUb3Pfq0rDaDW7/3wvCag/k1n9Sv+hT7QYL/+O+sNoNvvLaw2G1G9zz\nq3vDajf48ktfDavdYOXRdWG1G6w4tCas9kB07zfdPtec3hBWu8HqT9eH1R6pb13Rs8Fva71ZFjl0\nnxeA/opVpCtdkTxPBWE4Ywm1IOlworq6aeQdlDDgZGQko9okcYEg9OdmuG9aPV54imvgzOW/6e7P\nxb+cR/FvTiH36bmmK2eBfPLCcRS+eBL5G+abrmQE8taqN3HtL1cw4SuTTVfOAnn/H/fD/VoxXKty\nTVfOAvndN17D9SOVGHfPeNOVs/688bVX4fm0Gs5bM0xXzgJ559k/4OKeUmQvm0mujKnumYPPv4ez\nO89g9uo5pitngexdvweX/3geUx6ebrpyFsj+77yD0jfPYubjs01XzgIp2+tG0bbPkLd+nunKWSDu\n3cV994zZKlZ/dO833T6vfVwB9xvFcD2Ra7pyFsiVw5dRsrMQOavzTVfAdHz975vqwiqU7TmHGctm\nkStjKnSfF0BPog5vVQtb720gfcORjIxk87SVEfDBT4+MynfaOWsKhnoIAw53r8iETBi13Awv1sLw\n42a4b3RfdnRfzHR9TeUNuHqsAhMXTDKtwTUQRPISOJjnVHXP6PY3nM6pEBk3w7NGMEcmZIPDaJuQ\nSWFoQRCEYYJusdYOXwd2L90FT0lP5jWL1QJnTjqW73uSjVvT9bU3t2PnbdtuKGRtT7Nj9Yn1iEuK\n0zt4Bt3zEolX99zo9jeczqkgCIIQHvJUFQRBGCboFmvdvXQXaoqq+9Jgd3d2o6aoGruX7hoUX/+J\nAwD4an3YeRtfAFcX3fMSiVf33Oj2N5zOqSAIghAeMiETBEEYBugWXW31eOEpUdcc8pTUkAVidX1N\n5Q1BEwcDX60PTeUNSi2Qsr1u/Pc33jAtUAzon5dIvLrnRre/gTinTeUNcL9ZHNLvRnJOhyN+rx8N\nF+ujdlzR7i8ShtNYheHFnDUF7P9GG7JlURAEYRgQStHV1OwxQVooBWKzFt0yYL6rxyq4w8DVYxVw\nPa6Ofaotq8Xrd7/yxe8eqcCfsRcrj65D2ow0pcdb1dKziqOg6UojeV4i8eqeG29VC5rLif4q6P4i\nOac6Wx1177VAdBPIAPrJK8JNQGJsyzy3xw1flRf2TAdmLXOFvC0z3KQeRn+lb59Da2ULEsYnYuaj\ns0LuTzf5iE7cYaRjFQQhPGRCJgiCMAwwiq6qJhBc0VWuACyn6/omLuBfFDk9cDLWv33T9eeUGlds\n1kzX9eqeG0dmIkCF53fR/UVyTrfl/Qrot2Dnq/VhW96vsOkSc05TLEB98GC7k/nzVn+pHq/d8XLf\nz5f3XwLQU2JhzFR+Euet8eKVvF8DXT0/n/zlx33p3R3pdDIZnYk8ABz6x/dQvKOw72dflRenXzyJ\nDl8H7v0Jnd2zsbwRO2/9Yqto2VvnAPSkr0/JUhdGBoDDPziAM6+c7vu5tbIFp188ic72Tiz+16+Q\nvubKZuwoeLHvZ2PMa05vQNL4JNIXSdyh7lgFQdBDvuYQBEEYBkRSdFUHX22rln71uMlqDqGbbU+k\ndGolJxQ9Em80iU3gry2lN5U3BE3G+vCC3L5oc9iUkzEAQH03e68FTsZCaQ8kcDLWR1dvOwM3kafw\ne/03TMYCKd5RyG7RC5yMhdJu9Hdm+2mldmb7aba/wMlYKO1949GMO4xkrIIg6CETMkEQhGGCTtFV\nT7E61slMP/tmMeuj9E9+9hHro/SPfvgh66P0A999l/Vx+p+/vY/1UvpHPz7C+ij9j996i/VR+p/+\n772sj9I/+89PWR+lf/LCcdZH6Rf/oo47C0WvLqwKnowZdPXqCnQn8rpfHFw5fJn1Ufr1U1X06mh3\nr67g2sf8OCk9krhD3bEKgqCPTMgEQRCGCTGxMVi4ZQlWHl6LVUefxsrDa7FwyxI2psOZmw6LVV36\nxGK1kNvr4pP5LU2U7sjk65RReicRk2WmV53mXw453XPKZLJK6Jf2X2R9lH7t/XLWR+lXD/E+SvcU\nXWd9lF744knWR+nFvznF+ji9bM851kvpRds+Y32UXmMyqaD0kp3qVTUzveFCHeujdPcb/BcjlB5K\n3GG4YwlVFwQhfGRCJgiCMMywOWxIzR4T0jbFBKcDzhwiTiyHLmY88xuz2X+X0u/8x4Wsj9Lv/J93\n8z5Cn7NmLuvj9Jyn8lgvpc/7uy+xPkqf8jCf3ILSXStzWR+lz/2721gfpedvmM/6KD33af5acPqM\nZbNYL6XnrZ/H+ih92tIZrI/Sc1bnsz5Kn7w4OMlLKLrrCZNrT+iRxB3qjlUQBH1kQiYIgjDCWb7v\nSaTnZfStlFmsFqTnZWD5vidJT3JWKuLH2pVa/Fg7ma3NLPsbpbuWm7x4EvriLfezPk5f8iM6cQOn\nz1tvMiEj9Ie2LWN9lH7/T/6G9VG6WXZDSv/SpjtYH6Xr9gegJ5si9UYSAzLbolk2RUofO9MJa7xV\nqVnjrRg706nUzLIpUnpyVirsaerPkz1t4D9Puv1F6hUEQQ+ZkAmCIIxwYu2xWHHgKawr2ohHf/8Y\n1hVtxIoDTyHWzifafeqT9UEvZvY0O576ZD3rW3N6Q1jtBisOrQmr3eArrz0cVnsg9726NKx2g6+/\nuzKsdoN7fnVvWO0G977ytbDaDVYdfyasdoNH964Iqz3S/oCebIpBbyW9WRY5Vh5dF1a7wdrCjUGT\nMmu8FWsLN7K+1Z+q73+qvU8/of48rT4xOJ8n3f4i9QqCED6W7m5+3/5wpLq6aeQdlDDgZGQko7q6\naaiHIQwzhvN94/f64a1qgSMzMaysjOHWWzLQrUVV/HohTm/9FAUbb0XuSn6LWCB/+h97cX63G9OX\nu/A3v3ooZB8A7F2/B5f/eB5THp5uupIVyGfbPukbK7Uyprpn/rDuLVx55yImP5iNR175Rsj96R5j\nuDW6DI7//Fif747vLAjZp3sNgejVITOoK/Xg0v6LmPpANrkypkL3cxFqf/3vm2jWIRsI72ghIyNZ\nHaQ7QHzw0yMj8p12NBZ/5u4VmZAJo5bh/GItDB3D8b4xirxeeOc8misakTQpBdMenG5a5NVX78P2\n/K3obOvsazNWEOxj1FuaAKDD14HdS3fBU9JTQNlitcCZk47l+55kV+WiPU4guPYVgJBqX4VD4D2j\ne4zRPqe6vkhqX410wj2nw/FZMxqRCZkeMiG7EdmyKAiCMMI5uvkgTr94sqeodBfQfKURp188iaOb\nD7K+/pMcAOhs68T2/K2sb/fSXagpqkZ3b2bE7s5u1BRVY/fSXTfVOAH92lcGTeUNcL9ZzKYRD0T3\nGKN9TnV9urWvAvF7/Wi4WB92vatWjxflhz9Hq4cqvjawhDtO3XMa7XEKghB9+AACQRAEYVjj9/px\n4R11/aeL75zHnT9YqNy+WFfqCZrkGHS2daKu1KPcbtXq8aLmTLXSV3OmGq0erzKzo9/rR9neUqXv\n/N7SAR8nEFrtK2rLnM5KkN/rR8l/q9OUn/3vYvIYWz1e1BQR57SIP6elf1Sniy/74zmyP11fKLWv\nuG1v0V491EVnnH6vH+f3lSm1C/vKyHMa7XEKgjA0yIRMEARhBOOtakFzRaNSa77aBG9VC1KzxwRp\nodTaUk10PMU1bFFZT3ENshYFp832VrXAW9GstLVUNA/4OIHQal9REzJuJeiZs5uUHm9VC/zX25Ra\n+/U28hhDKe5NndPWqy3qsVxtIfvT9YVS+8r1OD0hM1aQDIwVJABYuGUJ6TNWDw0CVw9XHHiKHZMO\nOuP0VrWgpVy9/bC5nP4cRnucwshhNG4JHM7IVySCIAgjGEdmIpImpSi1pInJcGQmKrX0/Az236X0\n+DHxrI/SuzrUq1xmuu4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BVMGtAAJfFIddV7QRj/7+Mawr2ogVB55i6y7ZHDbkfF1d92v2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\nLQuyIUmzjySLisWYX1ubpIuS3lDhzFle0hvGmKykX5U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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a5ff68b2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.jointplot(x='fico',y='int.rate',data=df, color='purple', size=12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### lmplot to see if the trend differed between not.fully.paid and credit.policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x2a581656e48>"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a5816b4fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Wl318LWZHB+a+fZh791iPHR1DFl5nC4ITDMUgFoNQCEKD1mMslrrL1lo+y6of\nkmQsRlv5LKs+h3QZTncanSLX5RvZLt87nU6Bdm44t8G2ftUo5UIIMZXEHn6IyKpriFyylMiqa4g9\n/NCQ+mxtjFMbNhPy+EoeSSHEZCW3P6aRyE2r4MiwKaxHDhO5aRXeZzY4b7/rLejuAiNuFRhx6O7C\n2LPbWo/oMO3GHwsRjGXmCzMhFqUCq1PZ0H+CY263dbcytWDQRX3/Cau+ysex7pH5FjPTZSiNjZjD\nPyPpkerk9J/RFo871Ttxen8nToF2bltmjcRveLs1tS511bkNqXIhhJiqYg8/hPHwg+mC7i6Mhx8k\nhrVWsdjLC6aDfNsgIYQoFulUTic2DU1muWOgnp8/Ai6X9ZNZv+5xuPseKzBAX5/V8YxEweuBQHVq\n++v2vsCT868irqiYKCiYuEyD6/Y+D1zJ1e9v49FTLxrx/lcf2gasZeWCer63aT+9g1HiholLVags\n9XDnJc2p16o3ryb2wP0jjsGVcafRafF4PovL1ZtXD/nSkyo/iTDaToEgblvWKJ1IIcS0Y6wbJRhc\nRhtjW58RaE2pqICKCtv6XAYmHY9xkgfBKUQb5GS6r0sVQhSHdCpnEOOdvdCeMT0oEoH2NgzFCgpj\ndHfZb9jTndP2Zxx9F3/ThfSWVBBXFFTTxB/q54yj7wKwONyOuW09m8+4mPaK2dQFj3P1gVdYPMsD\nwP72PoKDUYzE9FjDNAkORtnf3jdpGrR873QKIcSM1TPK2sZEeb6B1lYuqB8S7C0p1zy+UyEITrHb\noFwC5gkhhB3pVM4kYfupQclyNVBNvLNzZH1VIKftN599Ob5YmJKoh6jLjScewxcLs/nsy7gQAJOX\nm5fweuOHiKoeDgVOwRuLcH7fW4AV6EfFQM2cHut2seHt1tSdO+Op9bx56kI2LTmXNl8V9eEerml7\nm/MzUoI4jbLmOwq7s/EcNl41O71949C0J8Ue5S3ESPpkH42f7McnhBiDqoA1y8SuHOepnUpjIzt6\nYVP9sOt/VTqP74sv7+bl43GiiorHNLh0tivn67/TndLx4nT9K2YqB6e0V0IIMRrpVE4nc5vsp8DO\nTUR19fnAMNLBchQF3G4osaYGlX3yToLf/d6IzdU1a3Pa/r1ZcznuLieuWtNfoy43YbcXxWdFPP33\nOZfxQt2C1H6jLg8vnHExSkcVXwCCA2GIZqTOME2IxugdSBftCKo82rw89by1JMCjzZfCka05pSXJ\ndxTWafsPx70HAAAgAElEQVRX9x8v6ihvIUbSJ/tofOjFFyf18QkhxkZds3bomsqMcnCe2vnmVat5\ndGc66E7y+q8urmMp8Pj6LWxpj6KYJl6s2ABb2g3mrN/C2tUrHK/fkyEITrbrMzevGmWrwpkJ61KF\nEMUh0V+nEe8zG9IdyKS5TakgPcrsWtvtlNmzAaj8/L2od38OAtVWhzFQjXr356xkzzls36e4iaku\nzESORROFmOqiT7HGLl6u1Wy3f3m2Ve6PDNrW+yPpXuXm05bZvibXtCT5Rgd02v6ZHR/ktX8nhQgn\nP9lD0g/86nF2VjXx7fk38ncf+gTfnn8jO6uaJs3xCSHGxn33PVnbGHX5Clz33ofS1AyqitLUjOve\n+1KDSZuU2VBXB16vtUOvF+rq2KxYbdOGg73p6OJJpmmVY12/ByIxWntDHOkapLU3xEAklro+55ty\nqhAm+vrslPZKCCFGI53K6ebURlATv1ZVtZ6nmFaZ12vddfR6069NUBeeg7poMcq8M1EXLUZdeM7Q\n7eNx626laVqP8XiqNqK4QWHET0SxAv9E1aEBgJKS5dd/sMO2/vqjO1P/bptt3+gny51GWfMdhXXa\n/mjXQNb6fBViJH0yjMZn83q3yaPNl9JaEsBUlNTdiB1BZaIPTQjhIFtKkJy237MbY+cOzAP7MXbu\nwNizO1XX2hNGKa9AObUR5fR51mN5Rer6GnTZd3yCLqujdKC9nxP9UaJxq+MZjZuc6I9yoKMPyD/l\nVCFM9PXZKe2VEEKMRjqV00jkc3fDK1utzh5Yj69stcoBQmGoq0+M8iqJUd56K58k6WmH5pHDYBqp\naTfJLwXm/v1DOpEAxOOY71qBeNwYqIaBiZL6UQ0DN9bxeNwuUJQh9SgKXrfVqVyjtnJ25yGiikpY\ndRNVVM7uPMQaNX13sOFU+5HqU061RqqdRlkbqnyY/X2YH7RgvnfQeuzvy3kU1mn/c6rLstbnqxAj\n6ZNhND6bTU3Z70YLISan5NTN0dqQVEqR7i5rYDKZUiSRq9KpvqHKR39vH62tXRxp7aa1tYv+3vT1\n2x/usz0uf7gfgEjcsK2PxKxypzul42Gir89LmgLceUkzcwKlqKrCnEApd17SLOsphRCOpFM5nbz2\nStZypbERpaICpXEuyrx51mPiOVjTDu2kpt0Ee+33nyif5VUwVJXMbqOhqszyWn9mC0/xpzqSyR8T\nhQWn+AH479NW8E71XDxGDF88gseI8U71XP47Yw3lygX1tiPVyVFUp1HWa8zj0N5uRa6FRATbdq42\nO+w/2zBO+7/p/FOz1uerECPpk2E0Ppv2WvsvT6PdpRZCTA5OUzezphTJob65/zidAzGiiSUWURQ6\nB2I09VvX7+sO/Ml2+2S5z23/lcebUa4uX4H7gW/h+cXjuB/41riv454M1+clTQH+9vr5fPvW8/jb\n6+dLh1IIkRMJ1DOdGPajsMlypyAIscP2eS5T026Gr1VJvcAq98+px320m7hhYioKimnlmvSfanXE\n/CVu/D4XfeE4Jtbs2Aqfi8oSK6XIhnBlohRIvQI2hP0kQgWlGrdNe9tp7Q3RUFnCNQvqUuVO9Yue\nX4/RC5vrzqGtpIr6UA9Xt+9mUbcCq5y/PDjt/6IzZ9N9SfOo9fkqRDj5yZ4WpbG5nvfiBnR3W51+\nrxcCgdTdaCHE5OQ4ddMhpYhT/fv7W6hRKgi6S4iqLjxGHH8sxPv7rTuUt3TvgZ1hfn/2FQRLKvCH\n+rjunRe5ZeAAAPNqyzFN6A3FiMYNPC6VyhI3Z9RW2L/vBJjs12chhBiNdCqnE1W171gm1k06NVbu\npiZi+w+O2Dw17cbns9KHZHYuFcUqB8IxA3+Zj95QjLhhoroU/CXu1NSi1p4w/hI3JtZaFk+iPrUe\nRnFjoBJXVEwFFBNcpkEQz4hjMjExTevRzmj1ZksLi02DxT1DO9BmMD1SnUtKkGzvv6QpkFcncjzC\nyRczJH2+bjr/VP5PWxDKh37RkzU9YryZPd2guqDWP9GHMiU4pQRxSiniVN9qeCgjQlk8MqS6LdFG\nKF4ftxx9g1t2b4R4DFxuqK5GqbcGNlcuqOdA+0FIXbetx8xrS7FTQsHEpgwRQohikemv08mFF+dW\nbvWGRtx5LPv4Wsy+PsyWI5gHD1qPfX2pO5nKdTfYRtZTrrsBAJ9boT8Sx6UqeN0qLtV67nWpqXq7\nIAmpegxiqtWhBDAViKkqPtId5WRI+GPdIUzTTIWE3364O6d6p/Uq+e4/X05rkmaCi86cLWt6xKRg\nxg3oPEH86DHMaHSiD2fSU29ejdnRgblvH+bePdZjR0eqDUmlpxq+XTKliEN9g2r/O6hPlCuNjSi1\ntSjz56MsWGg91tYOW484fCAw/bzY13eQa7wQYvoqWqdS0zRV07QHNU37k6ZpL2iadqbNa8o0Tdui\nadrZieceTdMe0zRtq6ZpLyXLRW68Dz4MZy8cWnj2QqucRFS+f/ifGJv+gLnnbYxNf7Ce59iYJYMX\nDJEIZpB4xSgbjviHbX11uX0QnOqKdHm+KUOc1qsUOyWJk4kOJz9ZyJoeMRkEI3F6w3HiAwNw7Chm\n7yjrygVgRW6luwuMREA3I24F20lEcHVKKeK++57UICVhK9K2ct0NqfqV5zVaweIiEas+EoF43Con\nt+t7mddNQ2UJc6tLaagsoczrHrfrO8g1XggxfRVz+utHgRJd1y/RNO1i4NvAzclKTdOWAQ8CmbeO\nbgTcuq4v1zTtWuAbwC1FPMZpxdi6BUVVYEgaEKtcXb6C2He+CR0ZjWMsBh3txL7zr3iXr2DgV4+j\nVFRAxdBph8bTv0ZdvsIKlpCMuppZv+5xuPsewjGDciVOb9ggrii4TJNKn5qa/hqOGcwq945Yz5Ks\n96pQHe6j11NGXFFxmQaV0QG85empZ609YQYiscQ+rCm0lRlTaJ1SfjhNAS52ShInEx1OXgiRwYRg\n1ORYXwyicUq7OjEH+mHWLBTPyGvhTGesexxcLutnePlXvghYHUcSncQR22/dAvv3ocw7I124f1+q\nDTu/EuL7n2dz7QLay2qo6+/k6o69nH/V7UBu1/d82o9CkGu8EGK6Kman8lJgA4Cu668kOpGZfMDH\ngEcyyvYBbk3TVKASGNf5Rk7rHCa7bCOg6vIVcGDkekkADlpBDGKHD2N2dEBX15D1KKm7kw5BFLwD\nQfoHo7gAVyLQTv9gnDn9QcAKB3+s26TMO/QLRzIcfP3xFvqUCnxGjJjqwm3E8Zlx6o+nG2GfW+Fw\n+6DVIcYkisKJsJuaxF1O6z1GfgEYkdJjlCnATtvnvP8xclyTJIQYdzHTpDdkUOIyqTJCuI8exQwE\noLIKRSlc/tSp3gY5BuIhkTZk3eNWWVUAdc3a1J1I46n1mH191t3OSBS8HghUpwc2n1rP+eF2zm8Z\neucw1caRfT2iz61wuDP9tSK5BOOk2488FOIaP9F/J+Ox7lQIMfUUs1NZCfRkPI9rmubWdT0GoOv6\nFgBN0zK36QNOA94BZgMfdnqT6uoy3Ik8h7V5BFMIvfgivQ9+HxeACzjWAg9+H3+glJIrrhjzfsdT\ne9tRsAuZ3naU2lo/HxjxkXUA8Ti1tX7awmE4npFaIx6D4x24Zs+ittZPa3U1RtfIIApqTQ21tX6U\ntmPgm5VRk+iwtR6jttbPmotP4z83vTti+1subqa21s/pnYd5/ZQLU+Ux1UWnt4KVx/amfrdKexvE\nMjulJsSiqO2t1NZe4vgeTr9np+2d6iHPv8O77qD3gX8ZUV75ydspmUHBQvI5h8Ii5zB/lVWlKImg\nMJVVpQCEgCqfCz8R1EgvrtpaFJ/91P2TMR3aoGxtBIDvVz8j+l8/TC+E6OnG/K8f4qvwUfn5e2l9\nV0dJzqZRgGgUOtpR33VTW+t3bOOceDxu7MYAvF5Xztf3fOV7jffv2T6hfyev7j/OL7dZA70ut0pH\nf5RfbmshUFXKRWfOLvr7F4pcH/Mn51AMV8xOZS+Q+RenJjuUWXwBeE7X9a9omjYX2Kxp2od0XR91\n7klLay/lPqvB6egIjvlgYz97FDM2MnJq188fw71wyZj3O57i9XPsR0DnzLXOjaLYpwVRFDo6ghht\n9utGYq1tdHQEMVffhvnwgyPqzY/dSkdHkFAoRk28h15fOTHVjduIURnuJxyN0tERZF6Vj08saxyR\nbmNelY+OjiDv1TRRE+pNhIt34zFi+GMh3quZm/rdDh7vpEb1jniPwWA4p/dw+j07be9Un+/fIQuX\nYH7ur0dM3wouXEIwn/1OIXmfQyHnkMJ84entGSTYM0hlVSm9PYOp8h6svk3Aq+Jr7QJ/pRVlNI+7\nltOhDcrWRgAEf/pz2zYo+NOfE/74XcQHBm3rY/0DdHQEnds4B32DUWrKRi7B6BvMrY0qiDyu8bW1\nfrom+O9k3SvvE4uOHKD+71cOMa8q/8GV8SDXx/zJOZROtZ1idiq3ADcBTyTWVL6VwzZdpKe8dgIe\nrLG4UR3s6OeUQAnVNeX5HOu0WOfglIcSrzcV/GCIxDpJMzQIbrcVCME0rU6oywUh68uU++57iMGo\nU5fq+0+wq+4sBrylmChE8KCaJvO6P0i91R/3dfDKe51EYwbvnxjA41JS02Zay2dBT5/1V5n8bhaP\n01aVvvtZ393GwerGoRHhTZO6nnSHeHHLbs7bnDE1yL8amqypQWZLi+30KjJ+z+ae3Ri7WjAMD4Ya\nxTQbU9uDc8qQJ7a18OzbrQRDMfwlbm44t4HblqWXDjtNHRqPcPJO06cmenqVEJNdzIDjIYMyt0ml\n2YNrcACzpgaltGxM+5sObZBTG+E4Pdbns9JixWLpNsjtTqWtUm9ezRuPrGdT/bm0+aqoD/dwTdvb\nLE22cWS/vjotwUhySlmVTS5TQ3c2nsPGq2anX9NYT67dQbOlhZ1VTSPOweJx+jsZj3WnQoipqZid\nyl8D12qathWri/ApTdNuByp0XX9olG2+A/xY07SXAC/wP3Rd78/2Jl99ajfXnl3LnWVe1EgMf8nI\nnIa5mA5r2ZyCFCjnfghz+xsj8kwqHzrP2j5QTbyzc0SQhVQOMbIHWeirmkXQl+7cmygEfeX0BaxO\n4Xc3vsvmd9oTHUKTaNS0ngP3rTwL32AfR8oCqa2jLg+dZQFqBtNfRJp7jrHt1HNTz2MuN51lAa56\n/3Xrs2/dQuyB+1OdRvPIIYy9e3B/+avWeSjxwYGMCH+RCLS3QZ2Vp+yNDVt4ZGc71p8fHDO8iedb\nWLrKuVP1sz8e5Ilt6S+HwVAs9fy2ZY2pkPVJyZD1wLitSUmGtE9KhrQH62/IqV4IkTYQMxmMx6n0\nmlS0t2OWlUNNDcrw66iDqdAG5TLYlK2NoCoAJ46PHLicZU2bVGbPtjrRyXzLpgnxOEptLWB1xh69\nAOjuhkiE1so6Hm1ajavxHJaA4/V15YL6IfVJyTyV+V6fc9k+3/d484wlPFo2P/W8tSTAo82Xogy+\ny/DAFcUwHutOhRBTU9E6lbquG8DnhhW/Y/O6KzP+3QfcdjLvY5jw3N4O3mzpYe0FjZw3t5rqMg8e\n18llS3G8yzdFZLvLZXq8tnkmTbfVES/75J0Ev/u9kfscJXfYcHsDc61fyJDUISbvVFlfil7S24e9\nvxUs52W9nftWngXhCHiS22bsI+Pu6qHAHGr6uwiWVBB1efDEo/hDfRwKzAEg/l8PWZ3EpESnMf7j\nhxLnJfv0tI27Wkh2KIeXL13leApYv81+tHjD263ctqwxa8j6cetUOgR0cgz4JMQMse/EIDWG890q\n04SesMFg1KAq3od3cACzugbFn/v0qMneBhVisEm58GLMZ36TLjBNiMVQErmUzd5eq0OZOY3YMDB7\nrPAMG/e2oZRXQPnQCOXJ66fT9TV5jR0+vTVZnu/1OZft832PTdqlcGTkUpXN81eMS6fSqWMuhJi5\ninmnclxUeVV6IgatwQjf23yQS5qqWHNRE/WVJVSWuHNe4+J0l29a2Ll95LpKRbHKgcrP30t/X3j0\nqUsOoqZis3uFiGn9DqKGdfdyuEhiUDpcUkZNpC+xptKFx4jjj4WIlKbvfraVz6IsGqIsOnSktL3c\nCgRhvrvP9thS5aEQ1NWPnP4aSoSUNzwMuLz0uktTEWgrY4O0xdMfKtv01p6BiO37B0PWcuLWnjBm\nf19qpB2vFwIBWtX0eSl2ZD2naXbTYRqeEIXw9RdaqPKqrD3HYFGViurQnkQM6BiMU+4xqDxxHLW/\nL+f0I5O9DSrEYJMSDmHOrh0RYVyJJAYO29rA4xk5/TUxUOh0/WztCdPf20dwIEoUBQ8m/jLPkOtr\ntuURuVyfs8llamgur8l2R7jN44c6Rhxjm3d81nc5dcyFEDPXlO9U/sNFNfzmQD9/PGqt+/vT4R7e\nOrabW5fMYfn8WqrLvfjcJzENaZRUE9NC8o7f8C9GGXcC1YXnwKLF6cbMJuflaI2dSzEZHj/ANMGj\nWudSNQ3iysg7yKppbdTQUM2Bo91YdxOTP9BQX516bX3fcWvt5TB1fSecPj1gTTHjyOERuTiTU8x8\nbhdHPOm6ZATaWVGrU/jEtpas01uryrx094/sWPpLrP/V6qNBjrVnjDJHItDeTn0ivsF4TI91mmY3\nFabhCTFeeiIGD+04QZPfzZozKzgr4NxB7I+ahGJxKuODlIWPYlZWQSDgOMiZ73rqYq6FNltasqec\nyuEYzJYWKC2FcCg9qFdamvOAldP10zsQpHMgRrLtiKLQORBjlieYOrZsd1ud9u8kl6mhTq/Jdozc\nvMra3jRH3K0dz+mnTnEFhBAz08nNEZ2E6src3K75+fvL6jmlzOo89kUNfvJqC/+24R32HO2heyCC\n4dBJTF7IzSOHwTRSF3Jj65bx+BjjY7Sw94ny0IsvZj0HTueoJmK//LU6Ue427VOaJMtP15rpLA8Q\ndVkdsKjLTWd5gNO05tRrr9Zfst1Hslw56yzbeuUsaw2KevNq2/rUFLPZo4RET5Q/+3arbfWGRPnq\nZfYdr1XnNgBwjf6ybf3V+6xzmG1qVKE4nQPHcyTEDLFmxzN4Y9Yg0eFgjH/b0c1Db/fQMThKeqYM\ncRO6QgYnBuPEurvh6AeYg4OO241VsdswM5JIORVPBHFPpJwyMwYlHY+hpMS66xiJAGZ6TXtJokNU\nX2+lEUm216ZpPa+rB5yvn0NSYmUee6I8293WnPbvYOWCetvyzKmhTq9xOsZc3kMIISbClO9Ulv/8\nR9QOdrGorpSvXFDDTaeX404MBu/tGOSffvsOv37tEMd6QoRswmAnOV3IpwPluhuylg/86nHb+uQ5\ncDpHleE+/NFBlETEPAUTf3SQqnBf8p1Qh+XKtJ5bv7D3T/Qzy1+Kp7QEfD48pSXM8pdy6MRA6vWL\nW3Zz+7b1NPS2oZoGDb1t3L5tPYtbdgPg+vRnrS8gXq+1X68X6upx/aU1hVddvgLXvfehNDWDqqI0\nNeO6977USHqkzE9NmRtP4jN4MKkpcxMpt6YWJaexDpcsv+vyedy2rNGaeg1Ulri5bVljanrsogPb\nuePQy5wy2IVqGpwy2MUdh15m0YE3gPGJrOd0DpzqhZgpPvHGU3z/ya9y6f5XU2U7OsJ8/dUT/PpA\nH4M2qR2GC8VN2gfiBAcimG2tmMc7MOPOndKTVfQ27Phxx3LnY8g+uKtUVlrTX5N3dBUFPB6UqirA\nun5efHwfPZ5SWkqr6fGUcvHxfanrZzhmUBPpw5NoZzxGnJpIH5HE78lpar/T9dnJkqYAd17SzJxA\nKaqqMCdQyp2XNA+5q+f0GqdjzOU9hBBiIkz56a/GM0/Bcxsou20Nddd+hA+fXs6SOh+PvRPk3Z4o\nUQPWv93B64e6ueOiJhbOraay1INr2BqJmbCOzPP1bxAFzN8/a0159flQrrsBz9e/AUDs8Mgpj5D7\nWrsGNYrdfbZ61coS448N0ukenvpFwR+zOo2tPWGCPX30m6qVkiQaQwmHaC3J+DP1+Vh8dC+Lj+4d\nuptkyPnlK3B/+atZ1yVlm2KWnFpUXjmsPDG1yF/ipmcgStw008ELFYVAWTrqcGYncjilsZHFRw6z\nuGfouVaamlPvf6C9L5FHzcTjUqgscXNmXWHXyzhNsxuPtCYTTdKmCCeRL32Nmh8/xBde+BE37tnE\nTy5ey7t1ZxAz4feHB9h6bJCPnF7BijklWddbmkBvxGAgBoF4EN/gIGbNLJTy/FJhDXmPYrdhDimn\nkseQNWVTKJx1TTuhMDScMmr9m2cs4ZWy+VRFB6mKWu/7yuz5nFmusAyrDToWVyiLD12CkGyDlMZG\ndvQyIh3H+VVKqj7b9TkXxrO/I/5+EMNXSTzci/G+Hz57+5DXZJs+6nSMTtsLS7FjEwghRprydyoB\niIQZ+MWjeO69m9qNTzOvDO47P8Admp/SxG3LI8Eo/7LxAD97cR+HjvcxEBk6Uqw0jtYJmF7ryFyr\nbkS94iqUheeiXnEVrlU3purcTU2YH7Rg7t2DuWe39fhBy9C1dn19mC1HMA8etB77+lL1wfpGgp7S\nVDAeE4Wgp5RgvXVuK8N9GMPX36gqlWFremxP+wmCpmvo9qaL7rb0esnU3VbTTP8w9C6sunwF7ge+\nhecXj+N+4FsjOgrG1i3EvvRFonesJfalLw6ZHuY0tWhRYxUxwxwyOytmmJzXWJX1vKeOzWFq6Wmz\nyjnRHyWaCAwUjZuc6I/SPGtsue+EvRkx3V3kLX7lSk586wdE7/krzho8zv9++gHue/4hZifWcPdF\nTR7bF+R/v97J3k77IF2ZkrktuwaixDvaMdvaMGP2sx9OVtHbsKqA1Yn0eq1BPK/Xep6RcooSX9bp\nrUpjIwwOwsCAta5yYAAGB4e0MUpFBUrjXJR586zHxHNIRD61sXm+dY1feZ79OUiWv3nVah5tvpTW\nkgCmoqTScbx5ZXrqv10bl+vU/20/fIxftKq0llQl9l/FL1pVtv3wsZy2z+UYhbNkbIJj3SFM00zF\nJth+eJQ8qUKIgpjynUrX5+9L5bgiGMT88cNUfOH/of71l7hqjo9/uLCGpXXWXSwT2HSwl//19G7+\n+FYLJ/oixBPh4qfLOrJsHSanL9Lxnh7o6Rm6nqWnB6M7cSE+S7P/wnCmtV5xT786MmOHAnsGrD+z\nlrKRAXascitya2fc/s8xs9y16kaorBw6Paqyckjn2On8ZDsHTlOLInGD6nJv6k63S1WoLvemOoFO\nnKaWvn+in1nl3lRKHI9LZVa5d8gUYJG/mTDdXRSI20P8Ix/jxL89xOBHb+XSljf5/pNf4+PbfoMv\nak1X/6A/zvff7OYHu7ppHXDuJA7ETNoG4vQF+621lr29eR9msdswdc1a6y5lJGLNdIlEIB4flnIq\neyAi01eSWJeZGNSNx611md7ETBOHz2BFPq1LLG8gsbyhLhX5dOmqFdy5uI5T1AgqJqeoEe5cXJfK\nMbxJmW27/WaldgxnZKSN7wdHKc/991vsY5wJxiM2gRBipCk//VVdeR3KZVfg27yB/kd+Af390N6G\n8r1vUX36esrv+BSfOXcRF3WE+eW+IF1hg86QwX/86QOWvdfJbRc20VRfRfkkD+eeC2PrFmJf+Vvo\ntTqG5p63MV75E+5//tec8g9GX33NfseJlCO8q4OvJLV/q0NXBfutdB3RZI7KYd8rUnfdVA92kuVx\nxYXV9R+a59IqTxzrU+tZf8FHee6URQTdJfhjIa4/9iZrMsLaZ5vWaDy13nZ6VmZY/GxTi1p7wlSb\nYapDGeHcSwInteYx29TS1p4wZV4XZd6hEYtzDTcvcjMTpruLwjLLyui/9c8ZXHkj5et/xZoXfsc1\n+17isWWreT5xp+ytExF2d3Zyxaml/Nlp5ZR7Rh+3HZLbMnYCb38fzJqN4nWOLmun2ClJ1IXnYHi9\nVhub/AClpUMjhDukbDJfe8U2ZYj52is5fYZcIp+eXwmLTryWvj5Wpu9etvaEGfSU0ut3p5cXeDyp\n66vx1HqUiooR0cFzTZvS5qu0LW8fVp5tamZrT9g2F2ch19UX20S3UeMRm0AIMdKU71QCKD4f5bff\nTvjSqzHWPY7x26etRuu9g3jv/xq1ixZz0cfv4qwLT+eZ9/p5vmUQE9jWOsie3+3jlgU1XLGokZqL\nLsEzhb+cx+7/Ryu/ZJJpQk83sW/8L7z/9/fO+QnD9hfiZMoRY8d2q0MJ6TuFvT0YOzKDGJhDYzEo\nqf+gYNrmqUwG9nEZcWKu4X+SCi4jPfK/zmhg3dwLU8+D7lLWzb0YPniNj+McMt54Zy87XdVsvvBG\n2ipmUd93gqsP/InFe/fYf/Zh8g057ySfcPPSscydpE0RY2VU1xD89F8xcMNHKH/iEe7940+4Yfdm\nfnLxWvaeMh/DhOdbBnmtNcSfnV7O5XNKR6zhz5TMbVkWG6Qy/AGuZPoR9eQnEhVzLXTsO/9qTVfN\nXDs6MEDsO/+KN/GeTimb6Om2UpAM7zhntFvZPsPKBfVDUi4lpSKnbt3CG4+st9YjnrvCWo/4yHqW\nJvbrc6sczpimbC0viFBTYR1PvoNN9eFeWktGLoWoC6fvVDqljcolLclkNhnaqKl+DoWYqqb89NdM\nit+P61N34/7PH6FceXW68XtzJxVf+QKNP/oOtwf6+bul1TRWWJ2XgZjJI2+d4Ju/3cNb7x6ldzCC\nOVVzVB47al9+9AMghzU3owWayOhA2upJlg/rUCaKkoXGKKc1WV4dHSUlSUb5c6eeb/ua5+Ystvbl\nMK1xp7+RxxZ/hFZ/Laai0uqv5bHFH2Gn3/7cDJdvyHkn+YabF7mZLtPdRXGpWTqD8TmN9N73FTr/\n8ZvMnV3OP/3fb/I3G39AXa+VvqI/ZvLEu33c/3onb58IO7YrA1ErSuxAZyL9yID99XDCvP+eY7nj\n/1dVowRKGa18GKflCds3vMyP5l3NzqpmjpTNYmdVMz+adzXbn0ten0f5HSSK812XuvI0+4BqK09L\n37h4mkUAACAASURBVKl0mpo51VOGTIY2aqqfQyGmqmlxp3I4pa4e9xf+BvOjq4n//CeY2607aerL\nL1L9yhbOv+5Gmj98K3/o9vHb9/uJGrCvO8o/bT7Mjad3cePiRurqqlLr2qaM0b60mOl1o5kjiEnJ\nBl+pqsLs6hq5vT/RIBqjhM9Plo/2nSlZ7lLtX5M4z1VuCIWC9PkqMBUFxTSpCPcRcKc3CnpLITpy\nzVLQawWycRpp3nzaBbb1m0+7gAtta4ZadGA7ZmU7m+vOoa2kivpQD1e372ZR0P59T1byy9Gmve20\n9oZoqCzhmgV1OYebF7kp9lRBMT1UeF14S10oLoXRVsXFzjqb7q/9M94dr3PBr37G0v/+Gr87ZyXr\nFv8Zg95SWgfi/MeuHhZUe1lzVgVzykdvdg0TusIGA7EIVdF2POXlUFOD4p4ETbXT9R/n/6/UNWsx\nHn5wxC4y12U6TZ3Mtjxhne90Or3pu6Qx1UWnt4J13maWAeGYyaxyz4jo2pG49Rmc2kgnyz57O/zw\nMTa+30u7r5K6cC8rT6u0yhOcpmY6tQGT3WRoo6b6ORRiqpoELVWePB4rObIN5fR5uP/hnzB27cT4\n6Y8xD+yHWAzP756m/oWN3HLTas6/4sM89l6Yd7qixAx4+kCQba37uONDs1g0/xQq/WUoWULFTyqj\nnQuPtWbRqcEvuexSBjdusqY4JZWVoV6y3Pp3zSzoaE8HWQAr+l+NfQCekRTse5XW+a1vP0LbrOYR\nVXXt6cbIX+bjRJ+JaaZ/J4piMrvMmn+qNDayLl43cs2l27p70FZ9Cl1xlR5fBYaiopoGVeE+VH/6\ni0i29S5KYyP0gqkomCiYSjIU/dycts+l3incvEzbLIyZkDZF5M/rUqgu92AMqPRGTSJ2QbkUhciS\nC+lcvJSSP27mpnWPcdW+Lfxq6c1s1C7HUFX2dkW4/7VOLptTyodPL8fvHX3QMhw36RiIUxHrwz84\nAIEAVFZNbFtUWWWtlcwcvEyuq8+Q7f8r9933EAOMdY9bU16rAqhr1uK+28ojnMvUyTc2bGHjrhZa\nDQ8NapSV5zWmAvEcqrAPZnOowrpDZaVsGtlGJqdFFmKwadlnb2dZlvpc0kY5pQx5YlsLz77dSjAU\nw1/i5oZzG0ZNYzUW+aTjmExtlIkVqd10yI8qhCiMKd+pVOacitnfj+Ky71gCqOctRvnWdzFffon4\nL34Gba0wMEDp479g4R9+x5dW386L2qU8ebCf/qjJ0f4433qlncuPBFm9qIE5p86mxGcfZGYyUW74\nMKbNFBPlhg+n/p21wV+4EH7/h1T4d8AahU5Ed1UuvBjzmd8MnSZrGCgXXmzVmwamMvKLkmImR7Kz\n38rsM1WCvnTDaioKQZ+fPjMdtMYdCQ/pUAKYpoIrYo3+/vdFq1l3JH0nM7nmUpnrZi0QVd10edId\nSENR6SqpxB+3ovY5rXd586rVPLozvaYyGe5dXVzHUuDV/cezbu+0fyf5jqQLIcbG51apdUMoZtAb\nMYja3bhTXYSuvJbQ8sspe+63fObpdaza8zw/vXgtu05diAn88eggr7eHuKG5nCsbS/GMMsXWBIIR\ng8GYQSDWia+vH3PWLJSSiVkXplx2xcj2xTRRLrvipPbjvvseSHQih3MKJvfGhi08srMdsNZAHjO8\niedbrI6l1wsRm+i7iTWcp80q57X30rNxkimbrslI2VTswaZcjiGbJ7a18MS29N3AYCiWel6IjuV0\naKPy/QxCiLGZ8p1KAKW8HHetH+Ie6O6G2MgOpqKqKJdfgXLJcowNv8N44pfQ24vS2Unlw//On536\nFIvX3MUvazRebQtjAi9+MMibxw+xVuti+YJTCMwOZE1wPdE8X/8GkfY2eO0VqzOoqnDhxXi+/o2c\nto/t2WNNde3qtO5GulxQXZOK7qqEQ5iza6GrC+IxcLmhuhol0aFzmv2qxuMYqmtEvZq487m3/kzb\n7TPL28PDo8NmlsOGXh+GyyRuWEGBFExcqsJzvT7WAl2GiukeuX2XYR3Xxr1tmP191t9RMrprIMCm\nve0saQokwr0zon6zUstS4JkdHzAQidMbimaMQntS22dbT5NTgy3TNnMy0dEHxfRV4lYpcasMxAyC\nEYOYXefS62PgplsYvPJaap9ex9d+/322n7KQn110G0cDDQzGTNYf6OOlo4OsPqOCRbO9o96FTOa2\nLIuFqIwew+X3Q6AaxTXyWlpMSjiEWVs36vU/KZ//95ymTm7c1UKXJ0Cvp4y4ouAyTSqjA2zc1cLS\nVXBafSX6sd7E9d9qKVyqwmn11t3UZMom6y6hgcelUlniLmjKJqe7fPkew7Nvt9qWb3i7tSCdyunQ\nRuX7GYQQYzMtOpVJSnk5lJdj9veP3rn0eHDddDPqNddirH8S46nfQCSM+sER5n7vfr6gLeDVj36G\nR/oDHA8ZdIcNfrirm1ePDvL/s3fm8XEUZ/r/VvccmpFG92Vblo0vGWzAF5dljhgHy2wI4U4gZDdZ\nfuQiG1iym92chJBs7t1kN5tjCQkk2QUbDIEEjDHGHDYGbGMbyyBsfMiSdR9zaDRn1++Pmhkd06Me\nIgt8zPP52NK81dVd3ZquqrfqfZ/nxnklTDutGnfB8SlEb2zZjPB5Ye7pQ0afF2PL5qw69MieRhWS\nlMyRMQzw9mMkmFFlSwu4XEq0OkkX73IN5UqY7FIOt2dyx5O1IjZzKv3hdiPDWZJ270CYmCS1myoR\nxCT0D6iJz4DNfJU/mLC3t3aDCbtrGwBzLOne327z0TOQzi4ougLquGNAdZ4L2xwbxwP7YA4nP9w2\nDbdNIxg18EUN4ibOpfQUErjpUwQv+xDzHv4jP370LtbPvZjViz7MgDOfrsE4v9rjZU6xnWtnFTDV\nkzkiJhiTDMbjFMV85AeDyJJSJX9xDDGWQyhbWhAVFVAxMsR0eK6csWUzse/dk5IUkUcOY7y5F9u/\nfA2ubLC8vlXo5F69iD5HfsoeF4I+Rz57I2pRcWFtCU1t6dmvC4fJdVhJNo0H2eyQjbcN/pC5Dmom\n+7vFyTBG5SRFcsjh/cEJxkSTHUR+PmLKFCgrB5v5IC3cbvSP/y22X96LtnKV2tUD7E1vsuz7/8gP\nX/kNDaVxkpFJO7vDfOOlDh7dsp+u5qPETMhi3m+Ml3VNer0qJzOZMyOl+uxPDNJ5edDZoRwtZMLh\n6hgZLjsGdCOekg9JQiDRjGSOptVepzXE8HxPE7vMsBtgJOxV3eYr5Ul7dZG5dkgyJydsum0BkYTd\nqn4O48fxwD6Yw0mCMdhfk3DbNapcOkVODT3D4UZFJf7P3kHg2z/mg3ofP1/9FS5vfDbV973dH+Xf\ntvXx+7d8eMPmfRioLrk/bNAViBDt7EJ2tCOjkYzHvxskF2PkkWaQRmoxxtiimFOzYUaN/+bXpmNE\n/L5fZ9UGK/bYgN18QTdpP9h0mPJgP+5YGIcRwx0LUx7s51CTcuwmuv+1YnY9Fm3w5JnvBWSyv1uc\nDGPUyXAPOeRwIuKkdCqTEAUF1s5lWRn6576A7T9/gTjvgpS98LXN3PKjz/LtI+uZnhjHQnHJg28H\nuGdTKzt3vkOgu/e4kh8ZL+uaDGVYxUvpV1rca6b5V8JeEB4wVRwpCKtdPC2D5og2jF1QZGAgTNoL\nIgOJ84rUv+F2i81Ulh/aZlq+/LCyW1GVO23mF3Ak7Dmq84nH8cA+mMPJAVFUDJVVaC7X2McJQYFd\no8qtU+TQMvqisWmn4f3yXcTu/Bc+0fEa/772LhY17wZUX7ilLcQ3t/bw1KEBc0KgBJLall7vAEZr\nK7K/f9xjkdViTDYyPHLf26bHZLKnnWtpPfpttyNqp4GmIWqnod92+9BuaYaQ36S9vb0PdzxCVdhH\nzWAfVWEf7niE9g6VwzjR/W82O2TjbcOq+dWm9oYM9neLk2GMOhnuIYccTkScVOGvmSAKCqCgABkI\nqPDOWPouo6iZiu0rX8d4cy/G/fch39yLMAzmrlvN9zc9zuMfuY2HC+cSNuCAL8o9r/ZwWfsgH5nj\no6KmQlG/v88YN+ua3a7+xWJqSVwIsNnUP4BQGCqrUqFNOOxQXAIJZ1QgkGKUVqVQdoCCSJDe/PR8\nhoLIIAClMkyv4cQYJvqtGQalcmigLpNhegwnctgxwjAok6oN1YFevHY3cU0nyTarGXGqAz0AlBS4\n6AuEUPM1Va4LKC1Qk8aFHgMOv5QmGbKwSN2DFVX5nEmFxGLxNGa/mRUFWdXPYfw4ntgHczjxIVwu\n9AoPSIfS6h3IrB8phKDAIXDbBYGoJBA1TJWeovPOpu9bP8Lz6ha+vPoB9jRu4Hfn3cCR0imEDXj8\n4AAvHR3kqpkFLK50muZbSiAQlQzG4hTGe3EPDCBLSxEWDnAmWC3GvFe5cmOFThblO/EOhNNyJovz\n1c5Ulb+L9rz0vrQqoR060f1vdZGTtv70xdnhO2TjbUMyb3LdMPbXhmPI/noyjFEnwz3kkMOJiFPC\nqUxCFBQg8/PVpCCDc6mdfgbi336IfHUr8Qd+By1HsIVCXP3gj6ivquVXf/MP7NKKiUt46nCQ7Z1h\nPl4XZMm0YgqqKxD2948ldrysa/a5dUR27kox5SUhZifYX2tq4EgzjMrjSU7Wi1x2+gejaTuWRW71\nTPqLKtJERUTCDtBQ6+IPR0buRBpAQ+1QyFNDrYv/OxInnppSSDTk0DGahoHGUCMEBhoi4YSuml/N\n6q2HsMVjiZYoxzm5yqtdeTX7H9zIW+4q/A43fVoetfZWFn94eaoN+zsD7G3z4Q/F6B2IMK3MnRqs\nrlg4hb1H0rU+h6+QjlUfIHbvrzNS7kN2dO/joYQ/0XE8sA/mcPJBOBxQXoEsLgG/HwL+jNqNmhAU\nOgT5dsFAxCAQNRE10DTC5y8jvOQ8Zm58mh8+9h88O/lsHlx8Jf48D71hg9/s9bGpxca1sz1MLzQf\nW+IS+kIGwWiI4kg7Nk8BlJS+ayKfrBdjZGLh0MRbFrNnI9/YnbYwmRxDxotV86t58JXDafZk/73C\n6OTXeiV+m4uopmM34nhig1wqu1LHLmhp5KyNw/JGPVdD7ZATOx65jhWnV/Gr599JW1QcvUO2b1sj\ne5sH8Qsbva0xage6WDSsDVb99/VLao6phMhoWEmaHA/IZozLSYrkkMN7C/2uu+56v9swLgSDkbsA\n8vOdBIPWuSVCCDU58HhUSGw0kjYxEEKoVdiGyxHlFcj9+yA0SMGAl4teX89kI8jeyXMJozEQk7zc\nHqLNF2KKESRfBy0v733RExNTazF27UJuexXa28HvQ5xzPrZP/F1W9T0zahl8+RWVRxk3lHNZVo7t\nC3cgptZCfgHGxg0qZ6a7BwYCoOnof/9pxNRaHtrWQtQkZEsTgusW1/DHba2oJz1MYxIwNJ0bzq3l\nf55tok+MIusRgkAgyKpzZwLQ+OYR9vgZJl0i0IAzCwXzT6/lf3b2ENVs6edw5nPd+dOpe/JB5Gtb\naSmqJqI78IQDXNG4gWvtPWiLFrP6tSM8rNUociChSILeLJkGus7802tTdO7JHMlIzKDxqMo5nTe5\nkL5wnM1NXYRjEkNKdE3DadNZMr2ESUV5lvVj9/5aiYMnQ5FDIeSObUgE2qLFKSKIQIKUIRCKsbvF\nS2VhHpOK1Gp4Nsccz8j2Xc4EMbUWMWkKtLeB34+YWot+8ydPKZKe8T7DkwH5+c5vjfccZuOL0DS1\nG+jxKIbsWHRM59Jp03DbVDa5acq1phObOYfw8pXM6mth1ZP3YRiSd8qnY2gafWGDzW0hugZjTPfY\ncWUIsY9LCEYlMhrFEQyAblNjXbbIL0C+ujXNrN/8STW2JAmwfF5Ags+LfHUrYtIUNT4AsqtLjT9J\nh1MIEALtI9fguXDpuL+Tod1vsLMtQESzI5Psr5EBVhUMMnlWLW1hwfbOEBHNhiEEujRwGjGWLJ7D\n5FnW92DVP1uhzRti++H+jP0/wENrN7PmSIyIUNE0EaGz12cgj7Yy//TaMfvvWZOLTvn3GqzHOKvy\nXP84fuSe4bEZY042nFI7lcMhhFA7bqmwWG8aW6zQdcRlDYiLLsF4/DGMtWsQg4NcuHMDC/du4f5L\nP8XGKQsAlQvzRneYG2ZHuLjWR2F1BdoxZuazQuzeXyPXP6U+OFU4kFz/FLHaaSN2ujIh7+KL0T58\nldoli/aDO/9dhTcFI3EgPfxV2UHEYkh95Gq7RCASz/2ANA/bGm5f1zyIpjmQUiIFCAkaknXNQW4A\nBnW7aernYOK6xsMPcU1/H9c0PjOi3GjdBrfcyrrmQQzNQRwtdX4dI3V+Kzr3J15vNWX2S1KZP7Wn\nHYw4xOLDVvL1VH3j4YdMz288/BDccmtWVOk5OvX3n30wh5MfQtOgsBAKCxXjuM+bIKhJh64Jip06\nBXaJP2IQjKV3UtLlZuDaG9FWrOK6Rx/kg4/exR+WXM0r0xcB8GpHmNc7Q1w2LZ8P1ubjNGEFSmpb\nBmMGxdFO8gYCUFqKsCvncix2V6vwVisNSQD2NamUiKTsiKarz/uzy6m0wobdLZREdUoG+of6T11n\nw+5+FjfAs6KcsCNECDtxoRHXNBwOUpJPxp/WqvF+VApH8h6s+mfL9qX6Xjni5/C+d13zIIj0Xefk\nGDNW/71ycXYh/Ce7pNKGNzsmVLorhxxy+OtwyjqVw5HKucwgRSLy8tCv/yhawyqM1Q9iPPUXCiJB\nPv/Uf3FJ9Wx+cemttLlK8Ecl9+71sbU9xMfnRphZVYCzvBzhNGciO9awckisEHr+eeTGZxClpVBa\nCoDc+AzGGfPQltZj/Glt6lmNOH9qUiHTHTqJ8swAPR4DPX0w1ePJMGQLph/AqzmIDWPbkQJiaHi1\nLJ+xt39Mu9X5rejcj/aZa40liRr8wTAMZw6WEqIxfMlqFu3LhggiR6eeQw7vLVJyVqEQ+HwwaN4P\n2DRBSZ5OgaGcy0ET59IoLiHwyc/iXHWUf3joAfb95VnuP+8GDpbXEpWCvxwKsrk1yEdmeTinKs9U\nOzluQE/IwBUboCg4iF5SgtG4B+PnP00dYya1M9ZiTDYEWMZbbyq2cJuu/gH4fcp+DNAe1UamrUgJ\nsRgdqGvt3ddG37CxIC40+nDSuO8orJyj2tE5zOFIsNMm8/gt+2cLHOgaGFNSCsAvzKdd/oSjOd7+\n+1SQVLJ6zrkxMIcc3h+c1Oyv7xYpKZLyClO2WFFYhH7Lp7H9/FeIiy4GYF77Pn7y4L9y3Y4n0KXa\nkdvTG+Gbr/Ty0Bs99B46QryrE5lB6uKYwsIhsULwwQxOaYL9z3JSkYl9MGFX5DnpXqeyZwdLt9NK\nlaQowyplwm51fis698kl5pT3SaIGT4KUKK1+JJhV+7KhSs/RqeeQw/sDkZeHqKyEyZNVeGwG2DVB\naZ5OpUsnL4MOSbx6Mr4v/gtTPnML9zQ9ymdf+B3FQS8A/VH43Zt+fvBaD+/0Zw5BG4xJOgbjBLp6\niT/0R6RJmG62UjvZSIoMMYWPQiZm8XeJqkCvqb0yQcTmz6D0lbJbtM+yf7aAlaQUgEeaN9Ij1WL2\nePvvU0FSKSfdlUMOxydyTqUJLJ3L6knY7vwytp/8DHH2AhzxGB/d8Sd+/MhdzO3YD0A4Lnl4f4Bv\nbu1l+4EeQoebkd7x076PCQuHxAqx5mZkIIBsOYI8cED9THwG60mFK2o+cUjapRCmO5kp7UjrjUoK\nnLriiBgmGSITdgBXzHzSkLRr195gWp60W53fis79ioVTTMuTRA0rW183LV95dGdW7VtxehXBSIx2\nX4gjfYO0+0IEI7ERRBA5OvUccnh/IewORGkZ1ExV/a9mPtTadUGZS6fCpZuGswLEZtXh++o9LPnw\nB/iPzb/k6p1/wZ6Ipjk8YPCj1/u5d3cfPYPmC5dSgjdi0NkzQDiuci6Hj0PZSu1kIylCpqicYxSt\ns7z9jQRRkKHyWKUBUrK8Yw+Q4A8ivf9O3a1F+1a2vo6BICp0IppOVOgYiFT/DCqf7wdPN/GPq3fz\ng6eb2NE8tGjr0DUMQxKNG0RiBtG4gWHIlKQUKLI5MyTJ5sbbf58KkkoOPSfdlUMOxyNy4a9jIBXS\nlCksduYsbHd/F+P1HcTvv4+pBw/w7Se+z4a5F/L7c68l6HBz2B/jO9v6uLTGzbWz41T6/eglperc\nxxjatTcokhcTezYQeXnQcmSIdCI0CIEALFqsznPl1cS++dWhfBndBiUl6LfdDkDFoI9me/qAWTGo\nVtftGERHh2oJgT1B3+PAICIVsUMKUuIQQxOgyRWFeI96Gc4HpAuYXFEEwIy4j716+ZCjCggpmRFX\nZAu2W24lBhnZVT0eN729wVF5oQKPR/29rOjcz5tVTv8F0zJSmV+rtcORrTxdfTZ+ex6eaIiV7bu4\n1taVVfsSDSLJfDv0+xBydOo55HB8QOg6FBcji4pUX+rzpY0jAA5dUO7SCccMfFGZrlEpBJGF58DZ\ni7jixU0sf/In/N/sD7B55rkAbO+Jsquni0unulk5vcCUzCdeWUVPl8AdC+OJDqLruuINyFJqJxtJ\nEW3u6RjBIPT1QjyuiIxKStFOPyPbRzZmPuBCzQ87HmPjzPPp9FRQ6eti+TtbWVipdqBcsQgx3ZbG\nMO6KRVLt21F6GhsrzqDDXUpVsJflXXtZVKXGrVkuA080iM/uJo6GJtXnWS51xiQBTBJt/aHU50W1\nxZTm2+kKhNN4BUrcQ4RJN1xdD2s3s645iF/Y8cgoDbVuZWf8/fepIKk0szIfOmVOuiuHHI4z5JzK\nLGDlXGoLFyHOXoB8YRP88QEue+sFzjm8i99c8DFenrEEQ8IzR4Ls6Arx8ToPF4SiuPPzFO173rEL\nx8jOIcmM6DsH0lkMDQN54ID6dW+jIjgwEiviRhz6+zD2NqItrae/oMT0vP0FKj8ThxPMFtMTq8T1\nB7fz3LQlacX1h7YDFwIgfT6MUfMtQ5Jg8wNc+dhi8QTRjkBIiS4NhGsoLNV2y60Zc0z7g1Fz2ZPB\nkSFmctg/M2SiMteuvJq2J/fgsyvKe5/dRVteMdrly1LH7L7sejZMvXgkVXqiLBsiCAC5txFjdwuG\nYcfQokhZM4I23wrb121mw+4W2g071VqUFWfVsLjh+MnHsaKTP5UlVXI4/iCEUOGwHg9yMKicS5OQ\nUKdNo8IGoZiBP2IQGR3lp+mELr4U/YJl3Pr0n1m1/t/53YKPsL/yNGJoPH0kxMstA1wxu4ilk10j\n8i3FBfXIxx8laHMS0h14ooPkR6Nol1+R9X1YEmDNroPnnxupb+z3wazsJEWs8gGlrx8MBwihFg6F\nUGOUV/X/JS4dv0lkZIlLRZrs+sDV3PtGf0pypL2oigMV0/n0mcUsBp6tPpOSrn5KGJky8mzVfJaQ\nDQmaQBMCbfSu86iPN1xdz1hLvVaSHmP1b8dCUmk8sirvBVacXkVbfwi3Y+QUdvRO5MkuKZIb53I4\n3pBzKt8FUs5lIJCmcyk0DXHJckT9hRhP/pmSNQ/ypY2/5LX9Z/M/S2+ip6CUnpDBT3d5ebk9xE11\nhUwNhbEVFEBJCcJ2bP4UYzlMVpDd3eYFfSqPxXj4IbXyPEr/LEkE5NPM6euT9kHzNAhCCUfzcH7l\nyF1KACGUPYH9vjhSjLy+RLDPp07SFzEwdA0jQbYjhUAAfZGhQWWsATMYjqalhkoJwVA0VXf1tqHw\nIn8olvp8/ZIaXtnfzQMbGtXiQyTCUYeDB1q7YcU8FtUW87NgJc9Vz0/lmUY1neeq5yOCldyOGiTG\nqp8NEcT2dZv5/c5OQD33NsOR+Lw5K8dwvPUnGla7BVblOeTwfkK43OByIyMRtRg2MJB2TJ5NI8+m\nMRgz8EWMdCkSh5PgFddQ/oHL+NrjD7PjxU38YdGV9OaX4pM6f3w7wPMHvVwzr4y5Jeo91ubNB0Bu\n3YzR3Y2vajrhC5ZSOrUWrb8fiorGL4W1rwkqq9LYVbNlf7VimN1JMfcu/Qg+ZwExzUZbYSUHyqdx\ny87HOBeIFZWACVlarEgteD4cLKTbDXFDuRlRTSfscPJwsIjFQPugoZzh+DD2V12nI6T+AFYEMOGY\nQVm+I7GDZmDXNQrzbCNyKscLq/4tmx3lsWA1xh0PsNqJPBXGgFPhHnM48ZBzKv8KiIICZH5+IpzJ\nm3IujR3bMZ59BtneDvPORNhsLHl1K/Mf+Tr/u+RqnjrjA0ih8WpHmMbebq6bVcAHaw08wQFkUfEx\nGdTHRSVuQbQzXiKgTKdP7jweqJhmWn6gfMgeFuakPkl7QOoYQiQyaRLnFwK/VE6m1YAp43EwuUaS\naMlKUuSxp1+Hzs6hgkgEOjvZ8CIsuqmeF5s60x+ElLzU1MntK2bzzIuNY9bPhghiw+4Wkg7hcGzY\n3cLiBtPqaceNp/5Ew2q3IEcnn8OJAOFwQHkFsqQUAn7w+5UzMwwum4bLphGMGviiBvFRr78s8DB4\n4yeZ193FD9Y+xHp/Ho+dtZKIzUlLVOenO/s5u8Dg6nnlVLptyrFMOJcAMaArGKcg1osnEIDy8nFF\nz8iWFlOG8OH5fNFvfFVJX4XD4HQiLluF/e7vpOqbn1fVf+T0S+l1Db3DMc1Gr6uYtXMv5VygzzeI\nHLUtKBH0+RUBz/6uAWLDUiwkEJPwTmJRrsrfRXtecdrCaZVPpSdUFzlp60/fYU4SwKhymSYpdSwJ\nYrLp38YjqWQ1xh0vGGs391QYA06Fe8zhxEOOqOevhBAC4fHA5ClQWoaxayfxP9yPbDuqyAO6OpFt\nR9E//0XcF1/Mp7Y+xHcf/zdqe9WgORCV/O5NP3e/2suenjDRvj442ooMpq9aZ4tk6JA80gzSSIUO\nGVs2Z3eCTLulSfs4iYDeC0R083uIJKRMxhowAcToWVsCSbuVpEhra49peXtHHwDRDAvWyTC39va+\nMetbERQAtBvp5FIAHRnsadcaZ/2JhtVuQY5OPocTCULXEUXFMKVGkcM50hd03HaNKpdOkVPDtObj\nxwAAIABJREFUjM/HKK8geuttrLhuBT9uXM3F+7akynYFNO7e2s0jjd0ETTqgpLZlpz9EuPUosrfH\nlCU2q3uxIHOLfuOryCceG2JhDYeRTzxG9Btfzap+c/Fk0/LDCXsow6JbKHHfsdG5qglEE/YVRqdp\n+aVSOZVWBDDvBUHMRPdvVmPciYBTYQw4Fe4xhxMPOadynEg6l8ZLLyjna9ROo9z2CrYv3I79Zz9n\nzvQKfvjot7nptUdS7H1NfVG++XIPv3/TR18gDF1dyI52ZDQzTXwmjJdKXJtkzmxKpRootWtvUDmX\nkYiaFEQiYBhDREBZsLeOiWzqWxxjc9ixGUZSGhMhwWYY2BzKIfKHYioXdMQ9xFMDptOIohsxhucs\n6kYMp6H+XlaSIlX+LtPy5Eq33TAfmJN2q/ozK/Mpy7djT8ws7bqgLN+eIigAqNbSiUAAqjLYR2O8\n9ScaVnTxOTr5HE5ECCEU8/ikyVBVDS53WnmBXaPKrVPo0NBM+sJ47XRst9/Jzctm8p1tD1CXYCOP\nC40NnQbffOEozx/yEh+dmA7EDOgOGfR29RNvafmrFjitGGLl+qcS7K0j/8n1T2VV3zKaxuyYYZ/t\nGRh2k/ZFDcu46fBLTBrsQ5MGkwb7uOnwSyxaOUSic8HMMryDUY70DeIdjHLBzLLUztCi2mJuvmAa\nk4tdaJpgcrGLmy+Ydkx3jia6f7Ma404EnApjwKlwjzmceMg5lccKra1qxdnhGOFcyvbEDljtNOxf\nuwvnd77LVYMH+Mnab3JmqxKEjkp47MAAX9ncxSttg0QHBuHoUWRv77taMR4vlbheVgZFRUOOsRAq\nJLdUEe1oZ8wbSY+vaYoM6Ix5iTOIdKdPpP7LAtb1dWF+jJ5o8/SqQjS7jh0DhxHHjoFm15lepdhh\nPSKuxK2TE42EuLVHqLCz2kg/NmngjEdxxiM441Fs0mBaRIX4WkmKrNTM81KTK93LBs3/RssGW4Hs\nVsrdDhvVhXlMLXFRXZiH22EbKSlylvlqfyZ7tsdlW3+icTzsFuSQw0RihN7lKKZwIQQeh3IuPQ4t\nLQ0dIHrGmZR+6U7+ebrk9u0PUeFX/VJAc/DgwTDf3dRMY6e59uJgTNIRiBA42oHs7ETGst+h0pbW\no992O6J2GmgaonYa+m23D4ViZtKrTNit6tf2tZpWT9oLIgOKD1vKoX9AQVg5yDMr8rHpYsQQZ9NF\nalFOW1rP4puv5s7wXr7fuJo7w3tZfPNQCsmO5n5efqeHIpedqSUuilx2Xn6nZ4SsyKLaYv5p5Rx+\nfN1Z/NPKOcc8FHGi+zerMe5EwKkwBpwK95jDiYecU3mMMDxsJ+Vc2u2IySN1C7V5Z2L/wU+o+ewt\nfH3ng3z++fsoCKl8jtagwXdf6+XnO3vpCMaQPq8KiQ0EyAaipsZUZ3I4lXjs3l8TabiUyAWLiTRc\nSuzeX6fKbLW1ymn0eCDPpX4WFafqG39ai6ioQMypQ5x+hvpZUTFqJzSRr5L8N8wDLAn7TdudtF86\nt8K0fLj9xvNqTY9J2q9bMpV8HeIIIppOHEG+DtclckEauvYMWyE3Ur+v7GoE4PqFkykNB7DHYyDB\nHo9RGg5w3UIVXnX9khpOn+QhGjcIJ3TITp/kSeWaLLt6Oed3v43X7qLFVYLX7uL87rdTK91fvGg6\nl7Tuwh6PJs4f5ZLWXXzxIpU3ms1KudVK+OKGem5eUMkkLYKGZJIW4eYFlSNIdsbSWsum/ngx1vWt\nYPUM3ovdghxyeC8g7A5EeYVKsxiVp6gJQWHCuSywi/SlOyGInF9P3e2f5Tuuw9y4+y/kRZTzdlS4\n+K/GAP/9/EHaAukRCFKCN2zQ2eMjcqQF6fO9u4Yrys30XcMMep3D7drSemzf+xH2PzyE7Xs/GpEb\neM2hLTjiUSK6nbDNQUS344hHueaQCvf90L6X0KSBFIqkTQrQpMGH9r8EqPHB47SlWHE1IfA4banx\nAeB1H/y49Bz+ef4N/Lj0HF4fdutj5bG9V5jo/u36JTVcv6SGwjwbAijMs6VsJwpOhTHgVLjHHE48\nnDjxDMc5zGi8haah33AjVFam2DxBrTTrS5ehnXs+y9c/zcJHf8T9ZzTw4qzzkUKw8WiYnW2t3HRm\nGRfX5OPo6UYG/FBappzVTEjSuScRiUBnByRCh2L3/hrjVz8fYrYLd2D86ufEUKyxtjPOgKfWDZVH\nwjA4mKovW1qUgzua2S+xE3rh7DJefKtz5MTBMLhorlpR+0LoTe52npvW7C+E3gQaWPbOazxr1I4M\nITYky955DVbMBuDayGEejEaJ2oby++zRKNdGDgM1yL2NRP0DxB1uJII4EPUPIPc2Qm09V732J7Ys\n+DgHymtJaj3O6G7mqp2PAbeyuKGeSw+uZZ1fJ6rZyItHubQwmnKoVm9rYecR7/DmsfOIl9XbWrh+\nSQ27pszjqSk9+DQncaHRb3Pz1JRFzKmZl5IFmRTspTAcxO9w44kEmRTsHfoeLa1H8wG7W5CGgPx8\nU7IlK6p0ccY8hCgHbxhR5EQMW9Xc0dzPL59uxB+MEkXQ1ibZ39zNZ1bOSw1IY9XPBmMRRu1o7udX\nzx/AF4oSjUvavIO80znApy+ekbp+NpT2Yz0DK0r+HHI4kSDsdigrRxaXKEKfwBCpjy4ERU6dArvE\nFzUIRke9DzYb8Q+u4qILBznvqad4/LDGxlnnI4XGG0Y+ja/0cElBiFWLaimwj3T6ogZ0BWO4I10U\nBvyIffuQTz6RkQhuLEkQrmyAwiI1foxGYVFWz+FgfQOBiCulQyyFIGB3cbC+gXOBmX0tuCNBAs4C\npFDpD+5IkJn9QxEieXadSFym2Fnz7EOkOtvXbeb3r7Yknm2cNlCfE8zX7d4wvQMRfKEYcUOia0of\nURsWi5xN32VFqGclFfHo663sbvFiSNjfGcAXih7z/s5KNutEwHgkRU4EuY7cOJfD8YYJ26msq6vT\n6urqfllXV/dyXV3dprq6ulkmx7jr6uo219XVzR1m+9dEne11dXV/P1HtO9YYK2xHuNyISZN5PZLH\nD1/v584Xu/jh9l529sawX/43VPzsp9xWNchXnv1vKhM5db3Szn/u9vG9jQc57IsgQyFoO4rs6U4x\nkaZhXxN4ChUbbTisfnoKU3Tuxh8fUDYjsUNnGBCLKTsQ3rQpYZcjfsqtCaKfPCe0tynW23BI/Wxv\ngwRbYJpDCaBpvPCWWt29u/Ac02Yn7d8ZnGIqKfKdwaHd3pteCY1wKAGiNjs3vaJW4O/b1kbAoXKR\nRGKVPOBw89ttbQD816zLOFhWi5DJECk4WFbLf826DFCTipe9giIjTE24nyIjzMtewfZ16hn88ZV0\nUenh9n//3XP06S7iCUmTuNDo013c98hWANZs3MMjMy7E78wHAX5nPo/MuJA1G9VO6Y7mfv7gLaB9\n2lw4bSbt0+byB29BaicvSSPe1h9CSpmiER++02d1zOqNjfQGY0QTextRBL3B2Ig2WF1jLFgRRq3Z\n1kLPQCRFjpGURVmzXU387n/hAKu3taTyXJMMvUmW3vG2L4ccTlQIXUcUJ0h9yspHkPromqDEqVPp\n1nHZ0mNiZZ4L25w5XBNr5lvP/Tfzj74FgKFpbAy6+eamVjbubiFmkm8ZjEnaN7+G779/jnHwANKI\nmxLBGX9aaxotk4xm0c47X0XDjEixKEY7/4Khc2zZTOzLdxK96QZiX75zxPnXxCuQo8YYqWmsiato\nlkfmXEzE5sQRj+KMRdSups3JI7MuBtROo9uhU13oTKQPOHE79NRO4zMv7Elj4CUeV3YgEovTF4ym\nclLjhqQvGCWcIAJK9lOZ+q7k/Y3VP1r1b1//UyM7j3hTrOnJhc2v/6kx7e/21yCbezjeMd4xIjfG\n5JDDX4eJDH/9CJDX1NR0AfAvwI+HF9bV1S0BXgBmDrNdAiwF6oGLgamcQBgrbGdHcz+/39FBW1gg\nbTbagga/f8vP650hhMuF68abWPKNO/m+dysffmM9WiKXclsojy8/28raVw8zGDOUI9fagvT2I0eF\nFhlvvamEpm02cDrVT78P48296gBvvzmJQUISJPpGYkAVJMJXgXgcmagvfT6IRkflI0ZTwtOWoU2Z\n5FIS9lgG5tbh9oDNPAk9aT/qMc8naPWoScfmqWebliftSk4jHUm7yXxrhL0Ft2l5q+YCYJ2zlpHr\nv+r3dU71VbcKr8om/MrqmMNecxKoQwn7eEO8rAijDvWYE4Ac7lY5Xmu3mecAJxl6j4cQtBxyeD8h\nhEAUFAyR+riH8i7tmqA0T6fCpeMcRkxjNO7BePxR4l4vJW47n933NHds/h3VXvU+BW1O1vQ4+M4z\n77D7QGfa+BLfshmv3U2XLZ9I3Ejl+w9PfzDeelNFx0QigExFyyTHIO3KqxFTpqj0iTPmqZ9TpqSI\neKwcrrA0H2OS9ubiKablzSXKbsWY2WEvMC3vtKnn2x80JyvrH1R9pxW7OFj3j1b92+4Wr2l5Jvu7\nRTb3cLxjvGNEbozJIYe/DhMZ/roMWAfQ1NS0NeFEDocTuAr4/TDbSuAN4FGgEPgnq4uUlLix2VT4\nSkWFZ/ytniC8uOkAtlSYja5CRw2DF7rjLK9LTAhK8qn4yj/y+cNHqL//EX5VMJ8D5dMI2pzc3wmv\nPPoGt11Yy5lzp6CJKCLUj1ZejuZWjkx7NIJh4rhpsSgVFR5ax2DOq6jw0BqJmLPrRSJUVHg42tWJ\ntNuV42kYylnUdURXp+Wzn+jy5DFm9w9gCI2KCg9R3W4azxPV7VRUeOiQDlPft1PaR7Zh+HNKVMjm\n+n67C7MG+O0uKio89ASjw74nQ+gORrMqB6yPkdKUO0lII+trjIXOjqNgM5n8dRylosKDpgmEiXcu\nNEFFhQdvMGL6N/CH48ekfacKcs9i/DgxxhcPUIGMxTC8XqTPh0y8X1UomQ1vKE7P9q3Eh7+XNjez\nifKtlmfY7J3Ow1WLCDrdtDsK+cVhmL//LW5adhq1k0oA8Pb1gk3DQKPPUUR+LIwnHkZrb009m7HG\nIICqKxsIFbsIPrSaWHMzttpa3DdcT97Faiexd93jCJO+w/b0E5ReObZIbjZjyLTKAlp60wmKppa6\nqajwUOXror0wfWGy0t9FRYWHcNzApmvEDQOJ6kZ1TSMcU2NoIBwbs+8C6/5xrP4Nxl7YPBbf0Wzu\n4XhHNmPEWPeSG2OyQ+5Z5DAaE+lUFgLDl87idXV1tqamphhAU1PTZoC6urrhdcqBacCHgNOAx+vq\n6uY2NTVlDIjv61MDREWFh64ucyKY4wGHOwNpK78ARwJx+uz5I3IuKSxl7m23cNfeJtY/+wJras4j\nbHfyVl4Ft2/18eHndnH1yoUUlhZBp1eR6pSWELc7VHhrMidSCNB14naHejYOx5A+2HA4EuVj7DR2\ndfnVREXT0o6TUlo++4kuTx5TEB7A70xfbS4ID9DV5cduxIlq6YOF3YjT1eWnSkRoM9LzViu1aKIN\nMt0nTDhpXV1+CiID+B2Zr++JDuK3p++2eqKDdHX5KXPbTcW1Jxe7sioHLI+ZFvOx316SVl4b82d9\njbEQr5qsdhpGQUyeSleXn9oSF/s603cra0vU+YvcDvoH0ndTC/P0Y9K+UwHHe3/4XuBYTHhOlPFl\nCA6ku1TlXPr9ajwAHED+oXfw2/KIiZH9d3xgkAs+fxWLdu7kyd172VCzEEPT2WMv4ysv93NR/G0u\nXzqH/JJSZNfQTo0POwHNRlFxMeLAUYTHo8Ygk3Eubld9aleXH85YBN9apAJhAD/gTzzb6DuHkJ0d\n0NerxjFdh5JSYoaqqxtx4ib9t57ov2v7WnmnfHpa+bS+Vrq6/Fw4o5QHDrQNjbcOBxQXs2xJDV1d\nfpYfeIX/XXBFWv3lB15V/bvThj8UQxulF+xx6iPKRyPZd4F1/zhW/wagCXPHUhPZjZNWyOYejndY\njRFW73NujLHGidMnThxyTnU6JjL81YdaQk1dK+lQjoEe4OmmpqZIU1NTExACzClBTzCMpSmUzLmk\nskqFraLCm4rnzeWqz1/Pdyt6WNCl9MZiup21BXP48pMH2Pbw08TCYQgpCRKKTMgOhECUl6vfa82Z\nU6lVzKNaQb55eYLSXsyebapTKWbPUcdlkj9J2i3K9QwajsPteRm+sUn7h7xN2IwYIuH5CSQ2I8aH\nvE0ALKswX0dJ2lecVQOxqKK4HxxUP2PRlJyGM24e/pS0X0W76fWviB8FoCHcTCK+OFFT/d4QViGf\nx0Iuw+qY6+ZXUBoJYDdU7pDdiFMaCXDd/IqsrzEWrLTmrlsy1VRrM8nAePUS86j3JKV9jko9hxwy\nQ2gaorAIMaUGKipSY4q7oozKkJeiSBBNDvXForwchMC5cCFX3bySu+0HOLtD5eFLTeN5ezV3belk\nU+UZREdNGQwh8C5dTldrJ5HWVsScOWocczgAoX5WVqHNPX2ozhg5kzISgu6uobzGeBy6u5CJxdCL\n7eYstEn7NV07KQ32YUv0x7Z4lNJgH1d37QJgQUsjN722lkneDsWu7e3gptfWsqBF5SMuPHsGN25b\nS7VPlVf7Orhx21oWnj0DsJbbyEaOw6p/tOrfzqoxJzXKZH+3eC8kRcb6DhwLjHeMyI0xOeTw12Ei\ndyo3A1cAq+vq6s5HhbVa4SXgi3V1dT8BJgH5KEfzhMeK06v4/cuH0+zDOynhcoHLpUh5vP0QCmHX\nNWZfuIQvL4nw/MYd/O9gKT5nAa1FVdxNFSt+9SQ3nl5E2aWXQNxQu5P2BJHN6BiWAXNdMgbUrpGM\nZBC3DydYay9Yhty9a2SZlIjzVe6oK8/OYCTGyNhKiStPtacsT6MnItPKyxIe4YxJJexr86axx86c\nPLSrNjMfGr1G2jGzPOrzx+75Im13/5bNnulEdTu2eIx6/yE+ds8XAbjjxnp67t/MGz6JgUBDcmah\n4I4b1T2c3dLI2+/sZ32dItPxhAe4rOlFzq7uB+qZOtjLIVcZMW3o1bEZMWoHFYPrZ7//BaKfvof1\nBTOG6gcO8NHE9a9bPh+59iWerj4bvz0PTzTEyvZdXHf1MoAUk9uzb3bS7gtRXZjHpadXjpDL2N8Z\nYN2ednyhGIV5NhrmV49ggLM6x+KGej7NZjbsbqHDsFOlKac5yXBrVd8KyVxi4/FHkS1HEDVT0T58\nVcq+qLaYT188M+P5//aiGQQGwqwbxqDYMIxBcbztyyGHUwXCnQ/ufGQ4jPY3HyZ+7y/Jj4dxx8MM\n2JwEbC44fxiztKZTdlE9n4lE2PfsZh4Kl9FaVM2gPY+H7XW8cGYFVx/dxhm9h9AqKhDn16PNm08k\nLunqH8R93kW49+5NkaSlTjsqZzKJ4eyw2tJ66M4w3Pconc07Pvchen78GG/YyzCEhiYNzoz2cMed\nHwFgyd9dD//1WzZWn0lnQRmVgR6Wt7/Bkts+qa7/p7Us8DazwDtyp9B4/FFFttdwOQue38iCTfcO\nRfsUFqI33AGQ6oMy9U1W5an7ZOz+ETL3b9++ch5f/1Njiv1VE8qh/PaV8zgWyOYexgPL78AxwHjH\niNwYk0MOfx0m0ql8FPhgXV3dFpQX8cm6urobgYKmpqZfm1Voamr6c11d3UXAq6hd1M83NTVloDo9\nsfBuOimRlwd51Wp1tr8fQoPkuxysvPw8zuwL8tCmJl6wq1XDDaedx7befj713Z9zXl8fttIyRdYT\njam8zZLSIcHpzk41SI7OB0yENGXUwwwmQhX3NcGkyemSIgl22cHoaIcSQCTsEA2FQRu9YyuUHWjt\nH1ThTsOh67T0DaY+Hu4PgxjJ/gpwqF+dY0dzP7uq5mBEEiQSNhu7quawo7mfRbXF7Gjup0tz4c5T\nchZ2XdCl2YfKN21j3ZlX4svzKEkQl411cy9m5qY/ce4tt1KtRZGDvWnXr9KUQx56/nlm9TRz2FZI\nh4xSFfYyq6cZY8tmJReytJ7rgWuHTyiuvipryZDh4ttFLvUcXn6nh1mVBWmO5VgD4OKGehaPkaJk\nVd+KEl/dRAatuizOn40u2njo4nPI4VSCcDrRV10OBQUYjz4MR5rxVJbh+cAHCZ55FoGoMfI1dTiY\nveoDfMXv55WN23jUMQ1/XgGd+aX8cvZlzPW2cO2cQqacMUTqLgG/oRFwFlKkBcmT6YuUY5HUaEvr\nVdSN3a7CdpNOnc2mokZQ7NxHcKGh3nsNOIKL7euU5Ie2tJ6D7dDUPIhf2Ogrrea0ZUs4N9E3yZYW\nHi4/i/VTFqUknS5r3cG1LXuG2udwqnEoGX7rcA61b9i9ZpLbyKbvSo4FmWDVPx4rBzITZnYfpq6z\nhXbDTrUWZWZ3GDhGTqXVd+AYYbxyGzm5jhxyePeYMKeyqanJAD4zyvyWyXGXjPr8zxPVpvcb77aT\nEk4nVFUhIxHo70cfDDK1NJ8vXLmQ8w708sCeHjpsBfS7i/nJ2dewpHgmn3z9UapLSsCZyNvTNOUI\nwlD46egdzKR9DCIfUAOyKChIE+GWCZ1KZAZ214TdJ8w1NpP2YMR8/WC4PSDsprIlAUM5WPetb6Q3\nAsnI7hiC3gjc90wji/6+njXbWmjzhlKTgXAMBsJx1mxvYVFtMffPWUGvu4ikcxwTOr3uIh6Ys4Jz\nUeGxP9vjx2d3E9c0dMOgMBrk5vlqx/mltRv599mrCNjzkAjeKahkd+FU/vHpzSxJDJg7a+ax4QPl\nQ/pXNVUpDcsdzf38dMO+lA7awe4BGlt9fHHFbBbVFrPhzQ4GfIGUxqQdicdt59k3O0d8t7avUzuR\nyUnB8J3I5HXG0uAaayXc2LKZ2PfuSS0uyCOHMd7ci+1fvoa2tB5jy2ZWr32Jpyd9EP/kPDyxECvX\nvsT1MEKr8q/VAEvSvSeRpHuHocWbbM5/IuiQjYUTvf05vPfQL7wI/cKLFHNrIu+yMBYj3y4IRAwG\noiOXaDSPhwuu/AALOrpYv/ktnimaSVyz8VZRDfd0wIU7NnDFRafjqVXsqvLlzRjufHrdM8mLRymK\nDaLbdMVsemWDpdYxRcWqbLQec5H6Xv92r5dex1CYZ0wIeh0F/Havl8UNSg5jTTugqdxOv+ZgTTuI\nhI7wI7XnsaZ6EXGhI4WgVy9kzcwLEQUFfBQwXt/BI5MXs375JfjzCvCEAlz21iau2bEdSMhtvHoY\nYoq3wB+JqM8M7fBl815mtSg3gRirjdvXbeb3OztRmbjQZjgSnzenxhCrexzr/mSLOcN6ah6RRRsn\n+hnkkEMOfx0mMqcyh2ME4XAgKiuVc+hy49QFy2aV8v2GmVxeCXoiP2bbtAXcefm/8mThHKKdnUr+\nwzDQli5DdndBYaH5BcxyMc3aUWO+UilqslR+sZAUyQoWsiWHB82LDycif/e2+dJ5doC9R1VOTkvx\nJMx2W48UTwJgfxD8drdiOJQqp8hvd7M/cf6f5Z2B3+5CJs4hEfjtLn7hmQ+ogeyBDY0cbdyPceAd\njjbu54ENjSn9q/teOmiqg3bfS4cAeKe521Rj8p3mrlRrk5OCNsOBRKQmBUmtzfHqoMV/8ytT2YD4\nfSoAYc3GPTw89Xz8Nhcg8NtcPDz1/GOmg2lF934stDyPd5zo7c/h/UUy75LJU6CsHN3hoMipU+XW\ncdvT+2NXVQUfcvn5+tM/Y1Hz7pT9xSln8fW3DDasfY5Yby+yuztVFtLtdDgL8Rsa8cOHFFFdXp5p\n35HUOtauvcG0vUn7Ybv5GJa0P7X9sIrSGSF7FWPdduX4PVF5FjHNhiE0JAJDaMQ0G09UnAnAI1PO\n4ZEFH8KfpxZO/XkFPLLgQzwy5Zyszp/Ne2klmzLRsGqjlayWVX2r+8tmHjHR/dsr+7tz/WcOOUwA\nck7lCYThzqVw51Pq0vl/50zirqUVzHCq3byQI4/fLL2Rr3/gNg4G4mCzI2bOgoEBxIeuVM6XlEOh\nibqOduMn1AVGh54mkbBbEQycCLDSmTSE+SuRtK9rHkRDYpcGDhnHLg00JOualVfZ5TSf9CTtz7zY\nqMKQk0y/kQh0drLhReVwtZowzim78pYjA+Zec3hYvqzVpGC8Omhy3z7TcrlPhUGvc00zLV/nqs3q\n+law0po7FlqexztO9PbncHwgpXc5eQpUVqK7XZQ4dSrdOnm2kc6lfPF5ykI+Prl9Lbdvupepva0A\nhO1OHimZx11bungjvxoZHxlx4re76K6exsDhI0hj7GwW2y23Ii5bpT4kyHnEZauw3XJrssWZ7kRd\nK2x+fl/C7rflpRb8UveFwJ/QOV5fd6Fp/aTd6vzZvJdWOpUTDas2thvp6SUAHQm7VX2r+8tmHjHR\n/dsTr7dO6PlzyOFURc6pPAEx3LnU8/M5u9zJdz4wmY96/OTF1EC8r3IGX/7I1/ijZy6BT/8/4qv/\nDzF1KhQWDTmPNkXXrp2hwhq1igxEu2WKPVZbWo9+2+2I2mmgaYjaaei33f6ehu28f1Bep1+YR4z7\nE3meMoNTKhO7se3tfabl7R3KbmQIQU7aHVFzp9MRHXK0rCYFVk6ZleNtBb/dPabd6vpWGItJOdvz\nj7cN7zdO9PbncPxBuNyIqmqonoS9oICyPJ0Kl44zwdLMwFDO/cy+Fr606X+4+dVHKBpUUR49+SX8\nev4V/GzOKo7G7SPYvuMXXEhHX5C+sCReXpkgkxtih03m/RtbNsP+txEzZiJOPwMxYybsfzvrXTxP\n1JyMbshu4ZSayFEpe35W58/mvcw2/HOiYNXGas2csC/JG2BV3+r+splHTHT/drTP/O+Y6z9zyGF8\nyDmVJzCGO5f5hR6ufe1RvvPn73NWgh49rtlYu+BvuPNDX2Hnc69h/Oj7ilU2uZIci4G3P7WC6Dz3\nHKioVMQISYKEikq0RYtT14yvexJj00Zk4x6MTRuJr3vyPb/viYQ7Yr4TmLR7ZGzYTq+R+t2TIKWw\n68IsehZ7Qtesyt+FGap8yu5xmjutnjxlnyEHKA35sMeUnIs9FqE05GOGHNJ9tJoUWDmkGLAeAAAg\nAElEQVRlWoZ5V9KekpAZhaTd4zTf8S5M2K2ubwUruvdszj/eNrzfONHbn8PxC+F0IioqYPIUHMWF\nlLt0yvM07K6R3zlNwLmtb/DVLb/jctmOLa4I2fZXnMa/1X+K/6tewoB3AHH2IrR5Kvw/VDWZzuJK\nBqZMg+nTETVT1U5pIvRxvLt4DYOHTfvnhkHF9mrPMONJ2j1xc6fCEw8Pnd/0uur82byX404jGSes\n2piUzxqNpN2qfjb3py2tx/a9H2H/w0PYvvejtIXpie7fJpeYL3zm+s8cchgfck7lSYCkcym2bmF6\ndzNfWf+ffObF+ykKqnDF9qJKvnX5l/jPC27Cb3eNrByJYGzcgIzFcH/0BnC5wO1WRD9uN7hcqbCU\n6De+inz8UbWqLCWEQsjHHyX6ja8mGpJBh1IkdSjNy5N2tSAuh00KFL+ePszJ+UDrLrNTpOwzhPkK\nZNLudmim13A7lMPzkbefG6HhBqBJg4+8vQmABqcPQwiimk5EsxPVdAwhaHAqEeCVZ04yvf5Fs9Vu\n7wqjk6Cw0W730OIspt3uIShsXCqVU/nhBZNJpGum/gkBHz5bkS1dWhTHHRqgKtBDja+DqkAP7tAA\nlxYNhWWtOKuGoO6g3VlEi6uUdmcRQd2RmhSMVwdN//tbTbXo9E+pELVVi6eB3TaUKysE2G00LJ6W\n1fWtsKi2mJsvmMbkYheaJphc7OLmC6alSBaOhZbne4Edzf384Okm/nH1bn7wdNO7yuc5Htqfw8kN\nYbcjSsugZirO0hIqL1lGyaAX2yg9YXf9Uq5YfhbfWFLMEv+QVMeWGefwjUtvY8M7vcT+8oQ65wX1\nSARem4tOzUU4ZiClTI0xVrtcM6LmoflJ+zXVgrkd+4hqOmHdQVTTmduxj2sSEotzJhWiiVFyJ0Iy\nZ5Lq2xpmFGJoGlHdRkS3E9VtGJpGwwyVvnDd8vlce2QrnuggIPFEB7n2yFauW66ifbJ5L9/vNBKr\nNi5uqOfmBZVM0iJoSCZpEW5eUJki6bGqfyzub6L7tysWTpnQ8+eQw6mKiZQUyeG9RkwN9nYjxgeb\nXmTBkT388dxreHHW+QBsmlPPjqln8XevPMRF+7cObahFInC0FSMQUMLTwYRjFhoc2tUE5FN/Nr2s\nfOrPcPd3AB1zkvXEzpVdh7hJuV2VV9gl7aOjXiRUOIbq9NrMVxKT9o/vf467T1uVpmP58UObgA9S\nSJygCVNPIerZzXJJbPEoEdvQSqktHmVWYmFzZts+omUVkAhzlQgMCTPb96t7eHs3xEtGXj9uMGnf\nG7BiNkyZSrhfELbZiQuduKYRjoVg8tDqrpHMd01+HtbUBQe2Y3SG2FhxOp3uUiqDvSzvepMFgy7g\nRgDEGfMIHGwkGJNIBBFNR8tzIhJhzotqi3nh7S5e3N9DNGZgt2lcOKtshA7aJ+57jb7g0I5nidue\nYn/Vltbzxue+ls4um1htvn5JDW+0ekeyx04pGqEzOdb1ITtmvkySIotqi5F7G9mwa5gO59k170rL\nc6KRDYPt6m0tPDVMK27VKK1OK73SHHI4FhC6DiUl2O74Eq6uLlwbn2FQCnyeUuLnnK+kSoCKYjd/\n17mdC9/ewqPT6zlUNpWIzcFj81eyKdjPx356PwsuOYfogsXIF58nNhCgO7+A/AvrKZ2n+hZRU6MI\nXka3IbHL9eOOZ/lU6aX0uYcWvkqCXn7ctxG4nEfaJW9Un57KgTeExhvVc3mk/U1uABbgY09cjgjH\nMOKSBSindPaSediO7iYqh/p3pyaZvWSo72vb2YcvkkdU2PDZ8mibMW+ExqTx1JNsOOSj01lIZdjH\niumFLKo9O3U9bWk9a9pVfr5f2PDIGA21Lm4Ytltnxd49Vt8AY7OvZtP3jSU5ZVVfW1rP6z4yjg/Z\nYKL75/NmldN/wbTjWocyx06bw4mInFN5EqMi2McXnv8NSw+8ygMfuYO2YByfy8PPLrmF52edz62b\n/0C1X7H1yebDeO+5a8ihTGJggNh3voXjL+sVm6wZknYLSZK4mUM5zN4eMlLO2nC0h4bcql2Vs03P\nkbTfc9pKU8mRe6Zfxp8g3WlNXiNh/0XNhSMcSoCIzckvpyzjHOC7ZeeZnv+7peeyFnio350eY6Vp\nrB0s5AZgTbtkoDAf3TDQE+7igCOfNe1elgAPbj2c7pdLZb9+SQ2ypYWF0mBhy0hCAdkydM37XjpI\nIC5AiMTCgSAQh/teOsSiGxewelsLL+3vQQAOm6r30v4eJhe7uH5JDf+xYR/9weiIKN7+YJT/2LCP\n21fMZkdzP3/wFsC0uerZAX/wgkhofa7e1sKbbf5UyC/Am21+Vido/a2un2TmS2K0w2XlkBlbNnPW\nA//BWcMf0C4wCkeKa7+fOmRjEVEkn+HqbUO7Nv5QLPX5+iU1WeuV5pDDsYLc+jKi+RBy5ixchkFe\nPE7w0NsE9+5BnqHCW2XLEWb09nJHcxPba+bz+LwV9LuL6HcX84tpl3Daa4f52J5t1DjtxD1q+3Bg\n504i6zdRfO5i8i6/Avmrn6ddO7nL9bBRTTAvH+ew3dJgXj4PG1V8FFhTUIcxqn82NI01BXO4AXii\nOQT6qIVJTeOJ5hA3oCSpQlJDDOuEQ1LjvvWNLLqlnn//3808Fy9OrZNGdZv6/L+buePGemL3/pqz\nfvvLkX0PENMDKbKh1dtaWNOpQ5JhFljTOSR7MpakR8PNDZZ9Q5J9NfV3S7CvAiMcy4nScLQaH47F\nNY4FjmcdymwWHXPI4XhELvz1JIcuJec27+bHK6Zwxe6nU3kvu2rmc8c13+KxM1cSFxqxf/gcRnu7\n+UmOJpjSjoUkyFjIQHIzwm5xjKGZr5Nkso9Gt9Njau9K2KOaOQlO0h60m++kJu3N+eZkSEl7NINf\nnrRnk6+SZJAdLdCdZJB9ao/533ldwv7i/h7T8pcSditmPqvzW5VbMfONl33weIAVEYXVM8qxv+bw\nXiP5XgkhELqO5nCQLwzKNz9DoUNTw0BELTBqSM5peYOvbfg5l+/diCOm2K4Plk/ju5d8ht/VLmPQ\nF0AkomtiL2+mLxCmr2oqxif+HqbWmpK4PD1loUo/EDoRTScqVPrB05MXABDWzfvnpN072qFMIGlv\nDZqnaCTtL3Wbs78m7cbDD5k/u2F2y3d7d4uKEIpEFANuJALxeIq926r++93/5fqm8SP3DHM4UZHb\nqTyZMLUWTEKHmFqLp7aGv311DUsPbuM3F9zI/srTiNic/P6863hp5nl85qX7mdVtTkKQ2oGcNHnI\nwRyOyeb5CSciRtPNp9kF5hG+Iu2XTAeMC9qVV49YhU7Zh+WrxOXogFDV5Hji7+gPxTBD0h6NmU+s\nIgm7lUNkdX6rcitmvvGyDx4PqC5y0mYiH5MkirB6Rjn21xzea8iWFmRXF/T1KqdHT7CH22x4ptXg\n7u+nT8QYUJngADjjEVY1vcD5vkM8vfxGNgfV9/vV6YvYMfUsVu3dSEPL9lSIfyguCU+dhefTcymo\nKkfLH8nG6rW7iA2joZYCYmh4k4zTlv3z2DAyHJi0RzMsaqbs3gx50cPslu92VEulsgBq/I3F6Ehs\nj1rVf7/7v1zfNH7knmEOJypyO5UnEWz/+vX0XUMhlB0QeS7mdB3k7j9/n09sXY0rwWh6sLyWf7ny\nq/z2vOsZtJmwrtlsSJ8PcdW1pqGf2lXXTsTtTAh0Yb4VmLTbLNgBwZzdNWnUZQYyooS9ts98F25a\nwi5MZ0RDdm1pPWL5B5G9vciDB5C9vYjlHxwR1mnLsHNsS+QRJZlkRyNpt2d4CMlQVStmPqvzW5Vb\nMfMdC/bB9xtWRBRWzyjH/prDew0ZCamc+2SefTwO3V3IcBjhcGCrrKR49gyqBvtxBX0qLSIaBU2j\ntLKEz11Wx5eOPMfMPuX0xHQbT5x5Gf96yefZRRH259aDESfeuIe+//w5Rz/9OQK3/wPxF59PtUHo\nemKMS/ZxKsxfS8hk6Zpm2j/riXHLKtimQEZMyz0Juz0eGxkCkvhnT0QAUZQhNHGY3erdrgr0mpZX\nBnqyqv9+93+5vmn8yD3DHE5U5JzKkwixe76ZntcoJbF77lK/u9Vk3WnEuHLPen746N0sblasqVJo\n/PnMy7jjmm+xvebMkefIL0D2dCOf36icSk1To7Cmga4jtyY0xDKwu6bsFuV6BvbY4XaR4Rwiy2t8\nrL8xzXETSD7WvxeAi+sqzfxyLqpTk/0SmzTNeSyxKWNpnrmcRpFTvWrX7Hue0mAftrgKE7PFo5QG\n+7h6n5o4nVVoPutJ2o0tm5Ebn0GUliJOm4EoLUVufGaEjpsnz2bq9xYmJh2r5lebXqMhYb9wVplp\n+bKE3cohsjq/VbkVM997wT440bBisLV6Rjn21xzec3Sbh8XT0536Vfr96MEBSgZ9VAS6yYuFIBxG\nBpTk0WmL5/HFXY/wd9vXUjagtHl9rkLuPf+j3N1bTvN3f4j9D79FdnYQQ9Dd7aX7v39JZP16/j97\n5x0eR3X1/8/MbNNKq5VsVVuWjdu64Q4Y2zTbgCExnQBJSHgTXtIgIT15E/KDVEIqqcDLm0LACdim\nhtgYF4oNNriAcVvcbdlWs9qq7e6U3x93tkg7q1mQhWS83+fRs7vnztw7u9o9d86953y/hmGQ51KI\nBZJIcjzAzDPlii4cW5TULsXbhR2mDCuwDDqnDBO/uyuGuXEYOrG1R8kAh6GzaJi4yZ9zdJvlRzDn\nmLDL191g2Z5st/ttz6t+x7J9XvX2jM7vb/+X9U29R/YzzOJUhXL33Xf39zX0Cu3tkbsBcnPdtLdb\nrzKeLtB+ca91QyiE8vkvof3+/i5Bly/Szpx9bzIkVMeecWfRoRq0u728OnoWR/1ljK/Zg0cNQ7gT\n/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FqFcWC/eGxrjf+2p4wqQZcVs7BBMJHrssLkUaL9Om8zg9qacGpRwMCpRRnU1sR1Oc3x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YRk9C7FtsI0mu44EbpnPPn1awqVtDRUJG46RSiQduENdYMLWeA10B3vnoEOGoitMuJ0mOGNdK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nfH/2/vzuPjKO/Dj3+emb1Xu5Is67As2WCwx8ZcBnMEHE6HmCRgcAIEkvxo2iSlSXOnTXMR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\noX/jW+hfvBa1xCjTGQohy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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a5815eaeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(14,7))\n",
    "sns.lmplot(y='int.rate',x='fico',data=df,hue='credit.policy',\n",
    "           col='not.fully.paid',palette='Set1',size=6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up the Data\n",
    "## Categorical Features\n",
    "\n",
    "The **purpose** column as categorical. We transform them using dummy variables so sklearn will be able to understand them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.get_dummies(df,['purpose'],drop_first=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>not.fully.paid</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_small_business</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy  int.rate  installment  log.annual.inc    dti  fico  \\\n",
       "0              1    0.1189       829.10       11.350407  19.48   737   \n",
       "1              1    0.1071       228.22       11.082143  14.29   707   \n",
       "2              1    0.1357       366.86       10.373491  11.63   682   \n",
       "3              1    0.1008       162.34       11.350407   8.10   712   \n",
       "4              1    0.1426       102.92       11.299732  14.97   667   \n",
       "\n",
       "   days.with.cr.line  revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \\\n",
       "0        5639.958333      28854        52.1               0            0   \n",
       "1        2760.000000      33623        76.7               0            0   \n",
       "2        4710.000000       3511        25.6               1            0   \n",
       "3        2699.958333      33667        73.2               1            0   \n",
       "4        4066.000000       4740        39.5               0            1   \n",
       "\n",
       "   pub.rec  not.fully.paid  purpose_credit_card  purpose_debt_consolidation  \\\n",
       "0        0               0                    0                           1   \n",
       "1        0               0                    1                           0   \n",
       "2        0               0                    0                           1   \n",
       "3        0               0                    0                           1   \n",
       "4        0               0                    1                           0   \n",
       "\n",
       "   purpose_educational  purpose_home_improvement  purpose_major_purchase  \\\n",
       "0                    0                         0                       0   \n",
       "1                    0                         0                       0   \n",
       "2                    0                         0                       0   \n",
       "3                    0                         0                       0   \n",
       "4                    0                         0                       0   \n",
       "\n",
       "   purpose_small_business  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X = df_final.drop('not.fully.paid',axis=1)\n",
    "y = df_final['not.fully.paid']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>credit.policy</th>\n",
       "      <th>int.rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>log.annual.inc</th>\n",
       "      <th>dti</th>\n",
       "      <th>fico</th>\n",
       "      <th>days.with.cr.line</th>\n",
       "      <th>revol.bal</th>\n",
       "      <th>revol.util</th>\n",
       "      <th>inq.last.6mths</th>\n",
       "      <th>delinq.2yrs</th>\n",
       "      <th>pub.rec</th>\n",
       "      <th>purpose_credit_card</th>\n",
       "      <th>purpose_debt_consolidation</th>\n",
       "      <th>purpose_educational</th>\n",
       "      <th>purpose_home_improvement</th>\n",
       "      <th>purpose_major_purchase</th>\n",
       "      <th>purpose_small_business</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1189</td>\n",
       "      <td>829.10</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>19.48</td>\n",
       "      <td>737</td>\n",
       "      <td>5639.958333</td>\n",
       "      <td>28854</td>\n",
       "      <td>52.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1071</td>\n",
       "      <td>228.22</td>\n",
       "      <td>11.082143</td>\n",
       "      <td>14.29</td>\n",
       "      <td>707</td>\n",
       "      <td>2760.000000</td>\n",
       "      <td>33623</td>\n",
       "      <td>76.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1357</td>\n",
       "      <td>366.86</td>\n",
       "      <td>10.373491</td>\n",
       "      <td>11.63</td>\n",
       "      <td>682</td>\n",
       "      <td>4710.000000</td>\n",
       "      <td>3511</td>\n",
       "      <td>25.6</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1008</td>\n",
       "      <td>162.34</td>\n",
       "      <td>11.350407</td>\n",
       "      <td>8.10</td>\n",
       "      <td>712</td>\n",
       "      <td>2699.958333</td>\n",
       "      <td>33667</td>\n",
       "      <td>73.2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1426</td>\n",
       "      <td>102.92</td>\n",
       "      <td>11.299732</td>\n",
       "      <td>14.97</td>\n",
       "      <td>667</td>\n",
       "      <td>4066.000000</td>\n",
       "      <td>4740</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   credit.policy  int.rate  installment  log.annual.inc    dti  fico  \\\n",
       "0              1    0.1189       829.10       11.350407  19.48   737   \n",
       "1              1    0.1071       228.22       11.082143  14.29   707   \n",
       "2              1    0.1357       366.86       10.373491  11.63   682   \n",
       "3              1    0.1008       162.34       11.350407   8.10   712   \n",
       "4              1    0.1426       102.92       11.299732  14.97   667   \n",
       "\n",
       "   days.with.cr.line  revol.bal  revol.util  inq.last.6mths  delinq.2yrs  \\\n",
       "0        5639.958333      28854        52.1               0            0   \n",
       "1        2760.000000      33623        76.7               0            0   \n",
       "2        4710.000000       3511        25.6               1            0   \n",
       "3        2699.958333      33667        73.2               1            0   \n",
       "4        4066.000000       4740        39.5               0            1   \n",
       "\n",
       "   pub.rec  purpose_credit_card  purpose_debt_consolidation  \\\n",
       "0        0                    0                           1   \n",
       "1        0                    1                           0   \n",
       "2        0                    0                           1   \n",
       "3        0                    0                           1   \n",
       "4        0                    1                           0   \n",
       "\n",
       "   purpose_educational  purpose_home_improvement  purpose_major_purchase  \\\n",
       "0                    0                         0                       0   \n",
       "1                    0                         0                       0   \n",
       "2                    0                         0                       0   \n",
       "3                    0                         0                       0   \n",
       "4                    0                         0                       0   \n",
       "\n",
       "   purpose_small_business  \n",
       "0                       0  \n",
       "1                       0  \n",
       "2                       0  \n",
       "3                       0  \n",
       "4                       0  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training a Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Create an instance of DecisionTreeClassifier() called dtree and fit it to the training data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dtree = DecisionTreeClassifier(criterion='gini',max_depth=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            presort=False, random_state=None, splitter='best')"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtree.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation of Decision Tree\n",
    "**Create predictions from the test set and create a classification report and a confusion matrix.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = dtree.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.84      0.85      2394\n",
      "          1       0.24      0.25      0.25       480\n",
      "\n",
      "avg / total       0.75      0.74      0.75      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2016  378]\n",
      " [ 360  120]]\n",
      "Accuracy of prediction: 0.743\n"
     ]
    }
   ],
   "source": [
    "cm=confusion_matrix(y_test,predictions)\n",
    "print(cm)\n",
    "print (\"Accuracy of prediction:\",round((cm[0,0]+cm[1,1])/cm.sum(),3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training the Random Forest model\n",
    "\n",
    "Now its time to train our model!\n",
    "\n",
    "**Create an instance of the RandomForestClassifier class and fit it to our training data from the previous step.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier(n_estimators=600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_split=1e-07, min_samples_leaf=1,\n",
       "            min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
       "            n_estimators=600, n_jobs=1, oob_score=False, random_state=None,\n",
       "            verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions and Evaluation\n",
    "\n",
    "Let's predict off the y_test values and evaluate our model.\n",
    "\n",
    "** Predict the class of not.fully.paid for the X_test data.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc_pred = rfc.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Now create a classification report from the results. Do you get anything strange or some sort of warning?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cr = classification_report(y_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.85      0.84      0.84      2394\n",
      "          1       0.24      0.25      0.24       480\n",
      "\n",
      "avg / total       0.75      0.74      0.74      2874\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(cr)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Show the Confusion Matrix for the predictions.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2389    5]\n",
      " [ 472    8]]\n"
     ]
    }
   ],
   "source": [
    "cm = confusion_matrix(y_test,rfc_pred)\n",
    "print(cm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Running a loop with increasing number of trees in the random forest and checking accuracy of confusion matrix"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Criterion 'gini' or 'entropy'**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=10,max_depth=None,criterion='gini')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a58c703630>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Ku8TpwDzg6cC/ADcCP+pOaJIkSeqXukniPwJnZuYi4DfAizMzgQOAPbsVnCRJ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\nmZk1+R9oIbi2TL3QvgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a58c66f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (criterion: 'gini')\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=10,max_depth=None,criterion='entropy')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a58c9ee940>"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h4FMl458A1gQGAf2AtmT4ecDFwJzMtFsCe5jZfWb2azMbljMNIiIiItJL8uZ4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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a58c9ca1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (criterion: 'entropy')\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Fixing max tree depth**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=10,max_depth=None,criterion='gini')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a58ca58518>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MMCVJktQoE0xJkiQ1ygRTkiRJjTLBlCRJUqNMMCVJktQoE0xJkiQ1ygRTkiRJ\njTLBlCRJUqMaSzAjYmZTnyVJkqT2qpVgRsR5EfHIPtNfBPy6sagkSZLUWnVLMJ8IXBsR2wBExCoR\ncQzwY+CapoOTJElS+6xUc/5nAx8Hvh8RpwAvBGYDb87MbzcdnCRJktqnVoKZmQ9ExN7AOsD7gAXA\nqzLzwokITpIkSe1Ttw3mJsDlwOuB/wDOppRmfjIiVpuA+CRJktQydavIfwlcAWyWmbcARMS5wEnA\n64BoNjxJkiS1Td1OPv8JvKSTXAJk5nnA07EXuSRJkqjfBvOYYabfAby5kYgkSZLUaqMmmBHxO2DL\nzLwzIm4EhoaZdSgzrSKXJElazg1Sgnk2cH/191kTGIskSZKmgVETzMw8tGf+UzLzjxMXkiRJktqs\nbiefPYAVJyIQSZIkTQ91E8wfAe+JiFUnIhhJkiS1X91xMNcB3gjsGxG3A/O7X8zMpzQVmCRJktqp\nboJ5SfUjSZIk9VU3wfwxcEVmPtQ9saoyf3VjUUmSJKm16rbB/DGwdp/pTwDOGX84kiRJartBBlrf\nDdin+ncG8POIWNgz22wgG45NkiRJLTRIFfnplARyBeAwSknlvV2vDwH3AN9qOjhJkiS1zyADrc8H\njgSIiD8DX83MByY6MEmSJLVTrU4+mfnliHhGRDydxQOuzwBWBTbPzF2aDlCSJEntUivBjIi9gU8C\nD1MSyyFK1fkQpQOQJEmSlnN1e5HvTmmHuRowF3g88FTgWuD7zYYmSZKkNqqbYP4rcEZmLgD+F9gi\nMxPYC9i56eAkSZLUPnUTzLsppZcANwKbdP39hKaCkiRJUnvVTTAvBj4eEesCVwFvioi1gO2AOxuO\nTZIkSS1UN8HcG9gAeBvwNUpnnzuB44FPNxuaJEmS2qjuMEV/BJ4REatl5oMRsRXwCuDPmXn1hEQo\nSZKkVqlbgtkxMyLWA9YCrgT+Uv0vSZKk5VzdcTBfAZwGPKbnpc6YmCsu9SZJkiQtV2olmMBngJ8D\nJwDzmw9HkiRJbVc3wXwssF019qUkSZK0lLEMU/TMCYhDkiRJ00TdEsxdgSsj4uXALZRhihbJzCOb\nCkySJEntVDfB3B9YF3gtMK/ntSHABFOSJGk5VzfBfBewU2Z+eSKCkSRJUvvVbYM5H7hsIgKRJEnS\n9FA3wTwR+GhErDYRwUiSJKn96laRbwm8FHhLRNwOPNT9YmY+panAJEmS1E51E8wrqp8xiYgVKIO0\nbwo8ALys5q+sAAAgAElEQVQnM2/qen0HYC9gIXBqZp7Y9dqjgV8A22bmDWONQZIkSROrVoKZmYeO\nc3nbA6tl5vMiYkvgWOB1Xa8fAzwNuBe4PiK+mpl3RcTKwEn49CBJkqRlXt02mOO1FfADgMy8EnhO\nz+u/BtYCVmPx882hJJ5fAG6bnDAlSZI0VnWryMfrEcDdXf8vjIiVMnNB9f91lGrwecC3M/MfEbEj\nMDczfxgRBwyykNmz12CllVZsMm5NkDlzZk11CBqA26k93Fbt4bZqD7dVfZOdYP4T6N5KK3SSy4h4\nBvAaYANKFflZEfFmYGdgKCK2ATYDzoiI7TLzr8Mt5K677puo+NWgOXNmMXfuPVMdhkbhdmoPt1V7\nuK3aYzK21XRMYCc7wbwM+Dfg61UbzGu7Xrub0sZyfmYujIi/AbMz80WdGSLiYmDXkZJLSZIkTa3a\nCWZEPA54LrAKpZ3kIpl5zihvPxfYNiIur967U0S8A1gzM0+OiJOASyPiQeBm4PS68UmSJGlqzRga\nGhp9rkpE7EIZZqhfA8ehzFwmGj7OnXvP4F9KU8YqonZwO7WH26o93FbtMUlV5DNGn6td6pZgHgR8\nDjg4M/85AfFIkiSp5eoOU/Ro4HiTS0mSJA2nboJ5FfDsiQhEkiRJ00PdKvLTgRMi4tnAjZTHPS4y\nQCcfSZIkTXN1E8wvVb/37/PaEGCCKUmStJyr+yzyyX60pCRJklpmLONgzgBeCWwCPAT8BrgoMxc2\nHJskSZJaqFaCGRHrAOdTHtl4B2U8zNnANRGxbWbe2XyIkiRJapO6Vd7HVe/ZODMfnZnrUEoyZwCf\naDo4SZIktU/dBPO1wO6ZeUNnQmZeD+wBvK7JwCRJktROdRPMGcBdfabfCcwcfziSJElqu7oJ5hXA\nfhGx6Jnj1d/7Az9rMjBJkiS1U91e5PsBlwI3RcTV1bTNgbWAbZsMTJIkSe1UqwQzM6+j9CD/BqVK\nfAXgTGCjzPxF8+FJkiSpbWqPg5mZfwD2bT4USZIkTQejJpgR8SPgzZl5d/X3sDLz5Y1FJkmSpFYa\npATzL8DDXX9LkiRJwxo1wczMnfr9LUmSJPUzlmeRbwXckJl3RMS7gbcAVwJHZubDI79bkiRJ012t\nXuQR8X7gYmCTiHgmcDpl8PXdgcOaDk6SJEntU3eg9Q8C783Mi4G3A9dk5muAdwLvajg2SZIktVDd\nBPMJwPnV368Avlf9fSPw6KaCkiRJUnvVTTD/AmwYERsCTwd+WE3fCvhzk4FJkiSpnep28jkZ+Cbw\nAHBdZl5atcs8Bjio6eAkSZLUPrUSzMz8RERcD2wInFVNvgN4X2ae2XRwkqTl2Lx5rH7Gqaxw+208\nvO56zH/3zjBz5lRHpV5uJ/UxlkdF/nfP/19vLhxJkmDliy5gzQP3YaWbb140bbUzTuXeI47moa23\nmcLI1M3tpOHMGBoaGnjmiLgRGPYNmfmUJoIar7lz7xn8S2nKzJkzi7lz75nqMDQKt1N7TJttNW8e\ns7fZaomkpWPBhhty1wWXtr6EbFpsq+VgO8HkbKs5c2bNmNAFTIG6nXzOAs7u+vkacA3wGODzzYYm\nSVoerX7GqX2TFoCVbr6Z1c48bZIjUj9uJ42kbhvMQ/tNj4jdgK2B45sISpK0/Frh9ttGfH3F20Z+\nXZPD7aSR1C3BHM73gFc19FmSpOXYw+uuN+LrC9cb+XVNDreTRtJUgrk98M+GPkuStByb/+6dWbDh\nhn1fW7Dhhtz/7p0nOSL143bSSGpVkQ/TyWcW5Sk+BzcVlCRpOTZzJvcecfRSvZMXbLgh9x5xNKyx\nxhQGp0XcThpB3WGKzuoz7UHgiur55JIkjdtDW2/DXRdcympnnsaKt93GwvXWKyViE5G0OI7jmE3q\ndlKrjJpgRsSVwPaZ+Vfg98DXMvOBCY9MkrR8mzmT+3f9wIQuwnEcGzAJ20ntM0gbzE2BTkvd04BH\nTFw4kjTB5s1j9RM/y8yPHsDqJ34W5s2b6og0VebNWyq5hDLEzpoH7uO+IY3DIFXklwKXRcRfgRnA\nzyNiYb8ZM/OJTQYnSU2ytErdBhnH0ZI5aWwGSTDfDLwdeCRwOHAOcO9EBiVJjRultGq6PHVEg3Mc\nR2nijJpgZuY/gBMBIuJJwJGZ2fLnW0ktZoeEMZnWpVXVPsE/7mD1tR/lPjEgx3GUJk7dJ/nsFBGr\nR8SzgVUoVebdr1/eZHCSlmQV79hN19Kq3n1iTdwnBjX/3Tuz2jA3Ho7jKI1PrYHWI2I74DbgKuAy\nSvvMzs9PG49O0mJ2SBiXaVla5T4xPtU4jr2DhTuOozR+dZ/k80ngh8AzgQ16fuzgI02gQap4Nbzp\n+NQR94nx64zjeM9hR3Lfrh/gnsOO5K4LL7P0VxqnugOtrw+8OjNvmYBYJI1gulbxTppp+NQR94mG\nOI6j1Li6CeZvgA0BE0yN3WR1SJhmnWEmvYp3GnYcmW5PHZmSav9pdlxNuml4XE1bbqtxmTE01Pto\n8eFFxBso1eSfBG4Elniiz7LSyWfu3HsG/1KaVP06qXRKkJqskpqs5UyqefOYvc1Ww3ZIuOvCyxpL\nlKbl+puOJnGfAPeL8XL9tcdkb6s5c2bNGH2udqmbYD48wstDmbni+EMaPxPMZdRoF8OmxiGcrOVM\ngUk56U3j9TepJqmkb9IuhO4X4+P6a48p2FbTMcGsW0W+wYREoeXCZI1DOJ3HO5yMKt7pvP4my2QO\nJ9W9T8z6xx3cs/ajJqTa3/1ifFx/7eG2akbdcTD/CBARTwU2AR4CfpuZOQGxaZqZrA4J077jwwR3\nSJj262+iTcUTg6p9YtacWdw/d2Keg+F+MT6uv/ZwWzWjVoIZEasDXwX+rWvyUER8F3hLZt7fZHCa\nXiarQ8K0HO9wErn+xme6ln64X4yP668hk9D0xG3VjLrjYB4FbAq8GpgFrEVJNjehPKdcGtZkjUM4\nHcc7nEyuv/GZrqUf7hfj4/obv5UvuoDZ22zFmgcfyBpf+DxrHnwgs7fZipUvuqDR5bitmlE3wXwr\n8L7M/EFmzsvMezLze8BuwNubD0/TymQ9NcOnc4yP629cpm3ph/vF+EzF+ps3j9VP/CwzP3oAq5/4\n2XY/2Wkyn1rlvt6Iur3I/wk8JzN/1zP9KcCvMnP1huMbE3uRL+PmzZvwDgndy5kO4x1OicnaTtPN\nJA8d1G3OnFnMnaA2mIt4XI3PJB1X021IpNVP/CxrHnzgsK/fc9iRzTc9mcRz4HTsRV43wbwY+Flm\n7tcz/WjgRZm5RbPhjY0JZjtMysVQ4+Z2qm+qLu5uq/aY0G01DYdEmvnRA1jjC58f9vX7dv0A8w47\nckKWPRnH1XRMMOsOU3QQcGFEvADoDKr+fGBz4LVNBiZJbTXdnhikdpmOHc2mbdOTaazuMEWXRsSL\ngA8DrwHmA9dT2mX+ZgLik6R28vnWmiLTsaPZ/HfvzGrDJM52vFk21e3kA3ATcFBmPi0znwP8D/C3\nZsMSML0aaEuSJsW0LO2z403r1B0Hcwvg+8ApwL7V5MOBtSPi5Zn5q4bjW25N5pNAJEnTx3Qt7bPp\nSbvUbYN5LHAOcEDXtI2AzwOfArZuKK7l21Q8CUSSND1UpX3DdTRrdUJm05PWqJtgbga8OzMXdiZk\n5lBEHAdc02hky7EpaaA9CU9HkCRNDkv7NNXqJph3AhsDt/RMfzLg2BgNmewG2lbHS9I0ZGmfplDd\nBPMM4KSI2B+4upr2HOAI4OwmA1ueTWoDbavjm2EJ8Pi4/iRpWqmbYB4CrAN8EVgZmAEsoLTBHH6I\nfdUymQ20p+N4aZPNEuDxcf1J0vRTa5iizFyQmbsBjwKeS2mTuXZmfigzH5yIAJdLkzgcw3QcL21S\nTebzcacj158kTUt1SzAByMx7gV80HIu6TFYD7Wk5XtoksgR4fFx/kjQ9jSnB1CSZhAba03W8tMli\nCfD4uP4kaXqa1AQzIlYATgA2BR4A3pOZN3W9vgOwF7AQODUzT4yIFSltPgMYAnbNzOsmM+5pbTqP\nlzYJLAEeH9efJE1PY3lU5HhsD6yWmc8D9qcM3N7tGGAb4AXAXhExG/g3gMx8AXAQpce6GtSpjr/n\nsCO5b9cPcM9hR3LXhZfZwWIA89+981JtZTssAR6d60+SpqfJTjC3An4AkJlXUoY46vZrYC1gNUoP\n9aHM/A7w3ur1JwD/mJxQlzNVdfy8w44s1fKWXA7G5+OOj+tPkqalGUNDQwPPHBHPogxJtAmwau/r\nmbnKKO8/BfhWZn6/+v9PwBMzc0H1/7HATsA84NuZ+cGu934ZeD3wpsz80UjLWbBg4dBKK6048PeS\nxm3ePDj5ZLj1VnjsY+F97zM5qsP1J2n5NmOqA2ha3QTzGuBB4DRgfu/rmfnlUd5/HHBlZn69+v/W\nzHxs9fczgK8DWwD3AmdRksxvdL3/X4CfARtn5rDjl8yde8/gX0pTZs6cWcyd6wOglnVup/ZwW7WH\n26o9JmNbzZkza9olmHU7+QSweWb+ZozLu4zSpvLrEbElcG3Xa3dTktb5mbkwIv4GzI6IdwGPzcyP\nA/cBD1c/kiRJWgbVTTB/CTweGGuCeS6wbURcTikO3iki3gGsmZknR8RJwKUR8SBwM3A65YlBp0XE\nT6q/98zMpUpPJUmStGyoW0W+MSVJPBu4hZ6SxMw8p9Hoxsgq8nawiqgd3E7t4bZqD7dVe1hFPjZ1\nSzDfADyZ8kzyXkPAMpFgSpIkaerUTTD3pIxF+enMvG8C4pEkSVLL1R0Hc0XgKyaXkiRJGk7dBPNs\nYNeJCESSJEnTQ90q8jWAXSLi7ZRe3g91v5iZL28qMEmSJLVT3QRzBnbkkSRJ0ghqJZiZudNEBSJJ\nkqTpoW4JJhGxObA35XnkD1EGXT8+M69qODZJkiS1UK1OPhGxNeVxj48HvgtcCGxIefrOi5sPT5Ik\nSW1TtwTzSOCEzNyze2JEHAd8DHhhU4FJkiSpneoOU7QpcEKf6ScBzxx/OJIkSWq7ugnmXynV470e\nD9w7/nAkSZLUdnWryL8GfCEi3gdcUU17AXAi8M0mA5MkSVI71U0wDwU2Bs4Hhrqmfw3Yt6mgJEmS\n1F51E8wAXgdsRBmmaD5wfWbe0nRgkiRJaqe6CeYPgddm5tXAbycgHkmSJLVc3U4+dwGrTkQgkiRJ\nmh7qlmD+N/D9iDgPuIVSRb5IZh7ZVGCSJElqp7oJ5puAO4DnVz/dhigDsUuSJGk5NmqCGREfAE7P\nzHszc4NJiEmSJEktNkgbzE8CswEiYmFEzJnYkCRJktRmg1SR/xU4OSKuBGYA+0RE36f2ZOZhTQYn\nSZKk9hkkwdwNOAzYgdLO8k3Awj7zDVXzSZIkaTk2aoKZmT+kjH9JRDwMbJmZf5vowCRJktROtXqR\nZ2bdcTMlSZK0nDFhlCRJUqNMMCVJktQoE0xJkiQ1qlaCGRF7RMQjJyoYSZIktV/dEswPAbdFxLcj\nYruIWHEigpIkSVJ71Uowq0dFvgL4O/BlSrL56YjYbCKCkyRJUvvUboOZmZdk5i7AvwC7A3OASyPi\nVxHxwYhYu+kgJUmS1B7j6eSzPvB04BnAysAfgXcCf4iI7ccfmiRJktqo1kDrEfFo4O2URPJZwLXA\nl4CzM3NuNc/HgS8A32k2VEmSJLVBrQQT+AtwF3AOsEtm/m+fea4Eth5vYJIkSWqnugnmm4DvZuaC\nzoSIWC0z7+/8n5n/BfxXQ/FJkiSpZeq2wbwAOC0iDuqalhFxWkSs3mBckiRJaqm6CeanKW0vL+ia\n9l7gucAnmgpKkiRJ7VU3wXwdsGNmXtmZkJk/BN4DvLnJwCRJktROdRPMVYH5fab/E5g1/nAkSZLU\ndnUTzJ8Ah0fEzM6EiFgDOBi4tMnAJEmS1E51e5F/CLgE+EtE3FBNC+AeyiMkJUmStJyr+yzym4CN\ngf2Aq4HLgX2BjTLzt82HJ0mSpLapW4JJZt4NnNQ7vXc8TEmSJC2f6j4qch3gQMozyFesJs+gdP7Z\nGFi70egkSZLUOnU7+ZwEvIPyyMgXAX8CVgG2BI5oNjRJkiS1Ud0E82XAv2fmjsBvgU9n5guAE4DN\nGo5NkiRJLVQ3wVwDuL76+wbgmdXfJwIvbiooSZIktVfdBPOPwEbV38niUssFwOymgpIkSVJ71e1F\nfgZwVkT8O/A/wPkR8XvKGJi/bjo4SZIktU/dBPMIyqMiV8zMKyPiE8BhwJ+BdzUdnCRJktqnboJ5\nGHBKZv4RIDM/Bnys8agkSZLUWnXbYO7B4vEvJUmSpKXUTTB/BLwnIladiGAkSZLUfnWryNcB3gjs\nGxG3U9pjLpKZT2kqMEmSJLVT3QTzkupHkiRJ6qtWgpmZh05UIJIkSZoeaiWYEfGfI72emUeOLxxJ\nkiS1Xd0q8l36vP8xwEPAZYAJpiRJ0nKubhX5Br3TIuIRwGnApU0FJUmSpPaqO0zRUjLzn8BHgb3G\nH44kSZLabtwJZmUWsHZDnyVJkqQWa6KTzyOAtwMXNRKRJEmSWm28nXwAHgR+DIzYw1ySJEnLh3F3\n8qkjIlYATgA2BR4A3pOZN3W9vgOlLedC4NTMPDEiVgZOBdYHVgU+lpnnjScOSZIkTZxabTAjYoWI\nODwiduuadnVEHBwRMwb4iO2B1TLzecD+wLE9rx8DbAO8ANgrImYD7wT+npkvBF4JfK5OzJIkSZpc\ndTv5fBz4f8Afu6Z9EXgvcPAA798K+AFAZl4JPKfn9V8DawGrATOAIeAbwEeq12cAC2rGLEmSpElU\ntw3mDsA7MvPizoTMPDkifg+cAhwyyvsfAdzd9f/CiFgpMztJ43XAL4B5wLcz8x+dGSNiFvBN4KDR\ngpw9ew1WWmnF0b+NptycObOmOgQNwO3UHm6r9nBbtYfbqr66CebawF/7TP8TMGeA9/+TMqRRxwqd\n5DIingG8BtgAuBc4KyLenJnfiIjHAecCJ2TmOaMt5K677hsgFE21OXNmMXfuPVMdhkbhdmoPt1V7\nuK3aYzK21XRMYOtWkV8F7NmnveUHgF8O8P7LgFcDRMSWwLVdr90NzAfmZ+ZC4G/A7Ih4DPAjYL/M\nPLVmvJIkSZpkdUsw96eMd/myiPhFNe2ZwLqUDjijORfYNiIup7Sn3Cki3gGsWVW1nwRcGhEPAjcD\npwNHA7OBj0REpy3mqzJzfs3YJUmSNAlmDA0N1XpDRGxAGQ/z6cBDwG+Bz2fmbc2HNzZz595T70tp\nSlhF1A5up/ZwW7WH26o9JqmKfJCReFqlbgkmlHaUp2XmjQAR8RZKoilJkiTVHgdzC+BGlnyiz+HA\ndRGxaZOBSZIkqZ3qdvI5FjgHOKBr2kbAt4BPNRWUJEmS2qtugrkZcFzVyxuAzBwCjgM2bzIwSZIk\ntVPdBPNOYOM+058M2FpZkiRJtTv5nAGcFBH7A1dX054DHAGc3WRgy7R581j9jFNZ4fbbeHjd9Zj/\n7p1h5sypjkqSJGmZUDfBPARYh/L88ZVZ/GzwzwMHNhrZMmrliy5gzQP3YaWbb140bbUzTuXeI47m\noa23mcLIJEmSlg21x8EEiIg1gaAMT3RTZi5Tz2acsHEw581j9jZbLZFcdizYcEPuuuBSSzJrcBy4\ndnA7tYfbqj3cVu3hOJhjU7cNJhGxErAWMBf4BzAnIp4SETs0HdyyZvUzTu2bXAKsdPPNrHbmaZMc\nkSRJ0rKnVhV5RLwC+DIwp8/L85jm7TBXuH3khxWteNsy8zAjSZKkKVO3BPMo4GfANsB9wHbA+4G7\ngB0bjWwZ9PC66434+sL1Rn5dkiRpeVA3wXwqcGBm/hi4BngwM08C9gT2bjq4Zc38d+/Mgg037Pva\ngg035P537zzJEUmSJC176iaYD7F4vMsbgadXf/+EknxObzNncu8RRy+VZC7YcEPuPeJoWGONKQpM\nkiRp2VF3mKJfADsDBwPXAttSnuLzFGDhCO+bNh7aehvuuuBSVjvzNFa87TYWrrdeKbk0uZQkSQLG\nNg7m9yLibuBM4KMRcQ2wPnBus6Etw2bO5P5dPzDVUUiSJC2TalWRZ+bFlNLK72TmXOBFwE+BI4Hd\nGo9OkiRJrVO3BJPMvLXr7+uAPRqNSJIkSa1We6B1SZIkaSQmmJIkSWqUCaYkSZIaZYIpSZKkRtV9\nFvlM4IPA84BVgBndr2fmy5sLTZIkSW1Utxf5yZTnj58P3NF8OJIkSWq7ugnmdsCbM/MHExGMJEmS\n2q9uG8wHgJsmIhBJkiRND3UTzLOBD0bEjFHnlCRJ0nKpbhX5TOCdwOsj4mZKieYidvKRJElS3QRz\nReArExGIJEmSpodaCWZm7jRRgUiSJGl6qFuCSURsDuwNbAI8BPwGOD4zr2o4NkmSJLVQrU4+EbE1\ncBnweOC7wIXAhsClEfHi5sOTJElS29QtwTwSOCEz9+yeGBHHAR8DXthUYJIkSWqnusMUbQqc0Gf6\nScAzxx+OJEmS2q5ugvlXSvV4r8cD944/HEmSJLVd3SryrwFfiIj3AVdU014AnAh8s8nAJEmS1E51\nE8xDgY2B84GhrulfA/ZtKihJkiS1V91xMOcD20XExsDTgPnA9Zl5y0QEJ0mSpPYZNcGMiPUy87bO\n39Xkf1CGK6J7emc+SZIkLb8GKcH8c0Ssm5l/A25lyarxjhnV9BWbDE6SJEntM0iCuTVwZ/X3Sycw\nFkmSJE0DoyaYmXlJ178vBo7JzPu654mIRwCHAN3zSpIkaTk0SBvMRwFrVP8eDPxPRNzRM9uzgN2A\nDzcbniRJktpmkCryVwFfZnHby6v7zDMD+FZTQUmSJKm9BqkiPzMibqY89ecnwOtY3CYTSuJ5D3D9\nhEQoSZKkVhloHMzMvBwgIjYA7gPWzswbq2lvAX6cmQsnLEpJkiS1Rt1nkf8LkMAuXdMOB66LiM0a\ni0qSJEmtVTfBPBY4Bziga9pGlPaXxzUVlCRJktqrboK5GXBcd3V4Zg5RksvNmwxMkiRJ7VQ3wbwT\n2LjP9CdTOvpIkiRpOTdQJ58uZwAnRcT+LB6u6DnAEcDZTQYmSZKkdqqbYB4CrAN8EViZMv7lAuDz\nwIGNRiZJkqRWqpVgZuYCYLeI2AcI4CHgpt5HR0qSJGn5NcijItfLzNs6f3e9dHv1e+2IWBugM58k\nSZKWX4OUYP45ItbNzL8Bt7L4kZHdZlTTV2wyOEmSJLXPIAnm1ix+NOTW9E8wJUmSJGCwZ5Ff0vX3\nxRMajSRJklpvkDaYpw76YZm58/jCkSRJUtsNUkX+uK6/VwReAvwF+CXwIPBM4AnAuU0HJ0mSpPYZ\npIp8287fEXEs8EfgvZn5UDVtBvA5YOZEBSlJkqT2qPuoyPcAR3WSS1j0LPLjgTc1GZgkSZLaqW6C\nOY/+zyJ/LvD38YcjSZKktqv7qMgvAl+KiKdS2mDOAJ4PfBD4aMOxSZIkqYXG8izyBcB/AI+ppv0F\n+EhmHt9gXJIkSWqpus8iHwIOBw6PiEcBQ5lp1bgkSZIWqVuCSUTMBt4LbATsFxFvAq7LzBsGeO8K\nwAnApsADwHsy86au13cA9gIWAqdm5oldr20BfCIzX1I3ZkmSJE2eWp18IuIpwA3AzsAOwJqU3uM/\nj4jnD/AR2wOrZebzgP2BY3tePwbYBngBsFeVzBIR+wKnAKvViVeSJEmTr24v8k8B38zMoJRAArwD\n+Dpw1ADv3wr4AUBmXgk8p+f1XwNrURLJGSx+7vnNwBtqxipJkqQpULeKfEtKFfYimflwRBwF/GKA\n9z8CuLvr/4URsVJmLqj+v676nHnAtzPzH9UyvhUR6w8a5OzZa7DSSisOOrum0Jw5s6Y6BA3A7dQe\nbqv2cFu1h9uqvroJ5hCwep/pj2ZxieZI/gl0b6UVOsllRDwDeA2wAXAvcFZEvDkzv1EzRu666766\nb9EUmDNnFnPn3jPVYWgUbqf2cFu1h9uqPSZjW03HBLZuFfl5wMciYs3q/6GIeCLwaeC7A7z/MuDV\nABGxJXBt12t3A/OB+Zm5EPgbMLtmfJIkSZpidUswPwx8H7izeu9VwCOBnwF7D/D+c4FtI+JyShvL\nnSLiHcCamXlyRJwEXBoRD1LaXZ5eMz5JkiRNsRlDQ0Ojz1WJiEcDc4GXAZsBDwK/ycwLJya8sZk7\n957Bv5SmjFVE7eB2ag+3VXu4rdpjkqrIZ0zoAqZA3RLMnwNvyMwLgAsmIB5JkiS1XN02mDMYrDOP\nJEmSllN1SzBPBX4QEacBv6d0ylkkM89pKjBJkiS1U90E8yPV7//s89oQYIIpSZK0nKuVYGZm3Sp1\nSZIkLWcGSjAjYiawNXA/cEVm3juhUUmSJKm1Ri2RrJ6wczPwX8APgRsi4rkTHZgkSZLaaZAq76OA\nm4DnA1sACXx+IoOSJElSew2SYD4P+EBmXpmZVwO7AM+sqs0lSZKkJQySYM4C/tr5JzNvARYA60xU\nUJIkSWqvQRLMFYCHe6Y9RP0hjiRJkrQccNghSZIkNWrQUsgPRsS8nve9PyLu7J4pM49sLDJJkiS1\n0iAJ5p+Ad/RM+yvwxp5pQ4AJpiRJ0nJu1AQzM9efhDgkSZI0TdgGU5IkSY0ywZQkSVKjTDAlSZLU\nKBNMSZIkNcoEU5IkSY0ywZQkSVKjTDAlSZLUKBNMSZIkNcoEU5IkSY0ywZQkSVKjTDAlSZLUKBNM\nSZIkNcoEU5IkSY0ywZQkSVKjTDAlSZLUKBNMSZIkNcoEU5IkSY0ywZQkSVKjTDAlSZLUKBNMSZIk\nNcoEU5IkSY0ywZQkSVKjTDAlSZLUqBlDQ0NTHYMkSZKmEUswJUmS1CgTTEmSJDXKBFOSJEmNMsGU\nJElSo0wwJUmS1CgTTEmSJDVqpakOQNNfRKwMnAqsD6wKfAy4HjgdGAKuA3bPzIenKET1iIhHA78A\ntgUW4LZaJkXEAcB2wCrACcAluK2WOdU58MuUc+BCYBc8rpYpEbEF8InMfElEPIk+2yYidgHeR9l2\nH2sdP9UAAAgjSURBVMvM/5mygFvAEkxNhncCf8/MFwKvBD4HHAccVE2bAbxuCuNTl+pieBIwv5rk\ntloGRcRLgOcDLwBeDDwOt9Wy6tXASv+/vXsPlrqs4zj+PkcRkwEHx+ympTH4DYUISaeYAdGRi9SM\npTVGTQR2UWuEgcpUwBsSRoIjglYSmkYZSGkCRo5KozblJc3k8jGO4WSCBgZCHjh02P54fls/lj2y\nwO7ZdebzmjnD7vP8Lt89z5zdL89lH0mDgGuB6bitGkZEXArMBw7PivZqm4h4NzCe9Pc2ApgREV3r\nEe/bhRNM6wyLganZ4ybS//4GknpbAB4AzqpDXFbeDcAPgFey526rxjQC+AvwK+B+YCluq0b1AnBo\nRDQDPYBduK0aSQtwbu55ubY5DXhc0k5JW4F1wIc7Ncq3GSeYVnOStkvaFhHdgXuAKUCTpOI2UtuA\nI+sWoP1PRIwF/ilpRa7YbdWYjgY+CnwWuAhYCDS7rRrSdtLw+FrgNmAO/rtqGJKWkJL+onJt0wPY\nmjvGbbYPTjCtU0TEccAjwF2Sfgbk5xp1B7bUJTArdQEwLCJWAh8B7gSOydW7rRrHZmCFpDZJAnaw\n5wee26pxTCS11YlAf9J8zMNy9W6rxlLu8+mN7HFpuXXACabVXES8C/gt8B1JC7LiZ7I5ZABnA4/W\nIzbbk6Qhkk6XNBR4FhgDPOC2akiPASMjoiki3gt0Ax5yWzWkf/H/3q/XgS74PbCRlWubJ4DBEXF4\nRBwJ9CEtALIOeBW5dYYrgJ7A1IgozsWcAMyJiMOANaShc2tM3wRuc1s1FklLI2II6YOvGfgG8Dfc\nVo3oRmBBRDxK6rm8AngKt1Wj2us9T1J7RMwhJZvNwGRJO+oZZKNrKhQK+z7KzMzMzKxCHiI3MzMz\ns6pygmlmZmZmVeUE08zMzMyqygmmmZmZmVWVE0wzMzMzqyp/TZGZ1URErAfagX6S3iypWwmsk/SV\nGt37eNJX9gyW9Fgt7rEfsZwC/BToBdws6Vsl9UcAYyXdUo/4zMxqwT2YZlZLHwS+W+8g6uwy0jZ0\nJwEzytRPBC7t1IjMzGrMCaaZ1dKLwCURMajegdRRT+BZSS2SNpepb+rsgMzMas1D5GZWS3cAw4Ef\nR8SAcjtflBvOLi3LhtT/ALwfOIe07d5VwFpgLtAb+BPwJUktucsPiYgfkXpSnwbGS3o6u0czqXfx\nQuBoYDVwlaTlWf1Y4HLgIeALwH2SxpSJvy8wE/g4UACWApMkbcqmCXwgO24McIKk9blzxwLTsscF\n4AxgKHA6aa/x4aRh9ckR8SngGiCA9cB8YLak3dn5x5F2jBkOtAKPZHG8ktV/DJhF2mN+B7AcmCDp\n9dLXZGZ2sNyDaWa1VAC+DBwPXH2Q15pEShL7AfcB87Kf8cAQ4H3sPRw/ibQt30BgA7A8IrpldTOA\nccDXgP7AT4Bf5vYgBjgR6AEMKHPtYiL8OGl/6cGk5Lc/8GBEHAKcStpabhHwHuDvJZf4BfA94OWs\n/vdZ+VCgBTgFmB8Ro4CFwE3AyaQh9QnA1CyObsBKUmI5CBhB2pLw4Yg4LIvl16Rk+WRgVBbbDaWv\nycysGtyDaWY1JemFiLgSmBERi4s9iAfgSUmzACJiLnARcKOk32Vli4BPlpwzRdK9Wf044B/A6Ii4\nm5SgnSdpRXbs3IjoT+q1XJm7xjRJL3YQ09eBLcA4Sbuy+3yO1Bs6UtKyiGgDWiVtLD1ZUmtEbAfa\ni/URASkxv1pSa1Z2F3CLpAXZqS0R0Z20X/I0YDTQjbRYqD07ZzSwCTgPWEHqpd0IvCRpfUR8mpSE\nmplVnRNMM+sMs4HPALdHxMADvMa63ON/Z//mh8Nbga4l5xR7BJG0LSLWAn2BPtmxiyNid+74LsCr\nuecF0lB9R/qSEt9dufusiYhNWd2yt3xFHdtQTC4zA4BTI+LiXFkz8A5S7/AA4J3A1ixBLToC6CPp\n5xExi9Tje01EPAjcD9xzgPGZmb0lJ5hmVnOS2iPiAtI8yckVnFLuvWlXmbLdZcry2kueNwM7gbbs\n+bnsmbiWnrNbUhsda+2g/BDKx1up0uu2keZ5Lixz7MtZ/SrS6ym1BUDStyNiHvAJ0jzN24GvAmce\nRJxmZmV5DqaZdQpJq4DrSHMie+Wqiglcj1xZ7yrddkDxQUQcBXyIlIj9lZQAHitpXfGHtJhn3H5c\nfzWpZ7FL7j4nkVaOr67wGoUKjlkF9C6JtR8wnbQKfRVwArA5V/8aqee4X0T0iohbgY2S5kk6BxgD\nnBERx1QYp5lZxdyDaWad6XrSnMD+ubINpFXREyOihTTUO53KEq99+X5EbCb18s0kzUG8W1JbRMwm\nzQt9A3iKNH/zStKipErNBS4hDf3PICWWNwN/Ji2oqcQ2oGekse2XOjjmOmBZRDwPLCEtPvohsFzS\nzohYSOoZXhQRl5NWiV8PnEZKPncC5wNdI2ImKSk9nzTFYNN+vF4zs4q4B9PMOk02V3Ec8J9cWQH4\nInAU8BwpcbqMfQ9/V+JaYA7wJGnYemRuyHsKcCtpJfUa4GLgQkl3VHpxSa8Cw4BjSUnqvcAzwFn5\neZn7sISUYD9HGr4ud5/fkH5HnweeJ/2O7iR9xRLZfM1hwJvAw6SV7YcCZ0p6TdJW4GxSz/EfgSdI\nc1BHFb/myMysmpoKhWp0EpiZmZmZJe7BNDMzM7OqcoJpZmZmZlXlBNPMzMzMqsoJppmZmZlVlRNM\nMzMzM6sqJ5hmZmZmVlVOMM3MzMysqpxgmpmZmVlVOcE0MzMzs6r6L/4LYnlmcNVyAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a58ca485c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (max depth: None)\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=10,max_depth=5,criterion='gini')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a58cc14e10>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RJYzzAx9k/p5qZv3cfUr6exRR7TwBuNHd3zez3YCx7n6HmR2Z+e5I4CJ3f9LM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\nLRNGSZIk1TJhlCRJUi0TRkmSJNX6f2w7jixphrwwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a58ca91e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (max depth: 5)\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Minimum sample split criteria**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=2,max_depth=None,criterion='gini')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a58ca68d68>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KravGoPXUOLSuGofWVf41ewL9P5a8z3z/QiJsZusDOxK3G58PXG5muwM3As8k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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a58ce60e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (minimum sample split: 2)\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nsimu = 21\n",
    "accuracy=[0]*nsimu\n",
    "ntree = [0]*nsimu\n",
    "for i in range(1,nsimu):\n",
    "    rfc = RandomForestClassifier(n_estimators=i*5,min_samples_split=20,max_depth=None,criterion='gini')\n",
    "    rfc.fit(X_train, y_train)\n",
    "    rfc_pred = rfc.predict(X_test)\n",
    "    cm = confusion_matrix(y_test,rfc_pred)\n",
    "    accuracy[i] = (cm[0,0]+cm[1,1])/cm.sum()\n",
    "    ntree[i]=i*5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2a58ce4c908>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BoERbRERERGQAKNEWERERERkASrRFRERERAaAEm0RERERkQGgRFtEREREZAAo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\nI2KXzLxiIce6ur8QB64GzhzswZ955sXXHbiGzvj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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2a58ceaab38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x=ntree[1:nsimu],y=accuracy[1:nsimu],s=60,c='red')\n",
    "plt.title(\"Number of trees in the Random Forest vs. prediction accuracy (minimum sample split: 20)\", fontsize=18)\n",
    "plt.xlabel(\"Number of trees\", fontsize=15)\n",
    "plt.ylabel(\"Prediction accuracy from confusion matrix\", fontsize=15)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
